The prevalence of cirrhosis is higher than previously estimated. Many cases may be undiagnosed, and more than half are potentially preventable by controlling diabetes, alcohol abuse, and viral hepatitis. Public health efforts are needed to reduce this disease burden, particularly among racial/ethnic minorities and individuals at lower socioeconomic status.

Selected missense mutations in the proprotein convertase subtilisin/kexin type 9 serine protease gene (PCSK9) cause autosomal dominant hypercholesterolemia, whereas nonsense mutations in the same gene are associated with low plasma levels of low-density lipoprotein cholesterol (LDL-C). Here, DNA sequencing and chip-based oligonucleotide hybridization were used to determine whether other sequence variations in PCSK9 contribute to differences in LDL-C levels. The coding regions of PCSK9 were sequenced in the blacks and whites from the Dallas Heart Study (n=3,543) who had the lowest (<5th percentile) and highest (>95th percentile) plasma levels of LDL-C. Of the 17 missense variants identified, 3 (R46L, L253F, and A443T) were significantly and reproducibly associated with lower plasma levels of LDL-C (reductions ranging from 3.5% to 30%). None of the low-LDL-C variants were associated with increased hepatic triglyceride content, as measured by proton magnetic resonance spectroscopy. This finding is most consistent with the reduction in LDL-C being caused primarily by accelerating LDL clearance, rather than by reduced lipoprotein production. Association studies with 93 noncoding single-nucleotide polymorphisms (SNPs) at the PCSK9 locus identified 3 SNPs associated with modest differences in plasma LDL-C levels. Thus, a spectrum of sequence variations ranging in frequency (from 0.2% to 34%) and magnitude of effect (from a 3% increase to a 49% decrease) contribute to interindividual differences in LDL-C levels. These findings reveal that PCSK9 activity is a major determinant of plasma levels of LDL-C in humans and make it an attractive therapeutic target for LDL-C lowering.

Summary Current obesity prevention strategies recommend increasing daily physical activity, assuming that increased activity will lead to corresponding increases in total energy expenditure and prevent or reverse energy imbalance and weight gain [1-3]. Such Additive total energy expenditure models are supported by exercise intervention and accelerometry studies reporting positive correlations between physical activity and total energy expenditure [4], but challenged by ecological studies in humans and other species showing that more active populations do not have higher total energy expenditure [5-8]. Here we test a Constrained total energy expenditure model, in which total energy expenditure increases with physical activity at low activity levels but plateaus at higher activity levels as the body adapts to maintain total energy expenditure within a narrow range. We compared total energy expenditure, measured using doubly labeled water, against physical activity, measured using accelerometry, for a large (n=332) sample of adults living in five populations [9]. After adjusting for body size and composition total energy expenditure was positively correlated with physical activity, but the relationship was markedly stronger over the lower range of physical activity. For subjects in the upper range of physical activity, total energy expenditure plateaued, supporting a Constrained total energy expenditure model. Body fat percentage and activity intensity appear to modulate the metabolic response to physical activity. Models of energy balance employed in public health [1-3] should be revised to better reflect the constrained nature of total energy expenditure and the complex effects of physical activity on metabolic physiology.

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10−8), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.

The close correspondence between energy intake and expenditure over prolonged time periods, coupled with an apparent protection of the level of body adiposity in the face of perturbations of energy balance, has led to the idea that body fatness is regulated via mechanisms that control intake and energy expenditure. Two models have dominated the discussion of how this regulation might take place. The set point model is rooted in physiology, genetics and molecular biology, and suggests that there is an active feedback mechanism linking adipose tissue (stored energy) to intake and expenditure via a set point, presumably encoded in the brain. This model is consistent with many of the biological aspects of energy balance, but struggles to explain the many significant environmental and social influences on obesity, food intake and physical activity. More importantly, the set point model does not effectively explain the ‘obesity epidemic’ – the large increase in body weight and adiposity of a large proportion of individuals in many countries since the 1980s. An alternative model, called the settling point model, is based on the idea that there is passive feedback between the size of the body stores and aspects of expenditure. This model accommodates many of the social and environmental characteristics of energy balance, but struggles to explain some of the biological and genetic aspects. The shortcomings of these two models reflect their failure to address the gene-by-environment interactions that dominate the regulation of body weight. We discuss two additional models – the general intake model and the dual intervention point model – that address this issue and might offer better ways to understand how body fatness is controlled.