Data from numerous animal models support the notion that caloric restriction (CR) is associated with increased life span. Studies of the impact of CR on mitochondria in rodents and humans have led to the idea that CR promotes mitochondrial biogenesis, which, in turn, is associated with increased life span. New data from Lanza et al. dispute this long-held notion by demonstrating skeletal muscle of aging mice that the favorable impact of CR on mitochondria is associated with preservation of key aspects of mitochondrial function and that these impacts occur without increased mitochondrial biogenesis. In an initial set of experiments, the authors show that lifelong CR prevented age-related reductions in mitochondrial function. Specifically, older CR mice had more favorable respiratory function relative to ad libitum fed mice, when normalized for mitochondrial content. Mitochondrial efficiency and phosphorylation efficiency were also more favorable in the older CR mice. Against the backdrop of preserved mitochondrial function, the investigators observed that CR did not prevent the reduction of mitochondrial DNA with aging in either quadriceps or heart muscle, suggesting that CR does not inhibit reductions in mitochondrial abundance with age. To address the possibility that there are differences between gene expression and the abundance of their associated proteins, the investigators conducted a proteomic survey whose results indicated that CR did not enhance mitochondrial biogenesis despite the robust observation that CR mice had more favorable mitochondrial function. Additional experiments showed that one mechanism by which CR enhances mitochondrial function is by attenuating age-associated oxidative damage through increased antioxidant activity and decreased oxidant emission. The authors conclude that lifelong CR in mice increases both the function and efficiency of the mitochondria by reducing cellular oxidative damage. — Helaine E. Resnick, PhD, MPH

Lanza et al. Chronic caloric restriction preserves mitochondrial function in senescence without increasing mitochondrial biogenesis. Cell Metab 2012;16:777–788

There has long been controversy surrounding the utility of BMI as a public health and clinical tool. Proponents argue that BMI is easy for clinicians to measure and for patients to understand and that decades of research have linked elevated BMI with health risk. Detractors point out that BMI does not provide insight on body fat distribution or the distinction between lean and fat mass and that a small but compelling body of literature suggests that current BMI-based categories of obesity are not universally associated with elevated mortality risk. Given that high percentages of body fat, particularly visceral fat, have been strongly associated with diverse metabolic abnormalities, there is interest in understanding how well BMI correlates with components of the metabolic syndrome (blood pressure, fasting glucose, and HDL and LDL cholesterol) and whether these associations are “better” or “worse” than other anthropometric and body composition measures such as body fat measured by bioimpedance, waist circumference, fat mass index, and fat-free mass index. New research by Mooney et al. addressed these questions using data from more than 12,000 individuals and their spouses who received clinical examinations as part of an employer-based wellness program. Overall results indicated that no single measure was consistently or overwhelmingly a superior predictor of metabolic characteristics. For example, while BMI was the strongest predictor of blood pressure, fasting glucose was more strongly associated with waist circumference, and there was no clear distinction among measures in predicting cholesterol. Importantly, for all associations in which a non-BMI measure was a stronger predictor of a given metabolic measure, the standardized difference in performance (as measured by the area under the ROC curve) was 3% or less. The authors conclude that, relative to BMI, none of the other measures were consistently better in predicting levels of key metabolic variables, and in instances when differences were noted, their magnitude was small. They suggest that these data do not support use of alternate measures of body fat in the clinical setting and that BMI’s imprecision in estimating body fat does not translate into deficiencies in predicting metabolic risk. — Helaine E. Resnick, PhD, MPH

Mooney et al. Comparison of anthropometric and body composition measures as predictors of components of the metabolic syndrome in a clinical setting. Obes Res Clin Pract 2013;7:e55–e66

Activated protein C (aPC) has been shown to provide protection from experimental diabetic nephropathy (Nat Med 2007;13:1349–1358), but the mechanisms underpinning this effect have remained elusive. In a new report by Bock et al., aPC is shown to induce nephroprotection by epigenetically suppressing the glucose-induced expression of p66Shc , an important modulator of mitochondrial reactive oxygen species generation. aPC reduced glucose-induced p66Shc expression in vitro in podocytes by enhancing methylation and diminishing acetylation of the p66Shc promoter. In experimental diabetic animals, the expected decline in aPC and its contribution to nephropathy induction was mitigated in animals with a p66Shc ablation. These studies implicate mitochondrial oxidative pathways in the mediation of diabetic nephropathy and demonstrate novel targets for amelioration involving epigenetic modulation of this pathway. — Sharon Adler, MD

Bock et al. Activated protein C ameliorates diabetic nephropathy by epigenetically inhibiting the redox enzyme p66Shc. Proc Natl Acad Sci U S A 2013;110:648–653

Genome-wide association studies (GWAS) have provided a wealth of knowledge on genes in which common genetic variation (most typically, single nucleotide polymorphisms [SNPs]) may be related to disease susceptibility. Despite this progress, it is often the case that common variants shown to be associated with disease risk do not have functional relevance. As a result, it has been proposed that identifying a denser set of SNPs within promising regions will facilitate identification of genes that are causally linked with disease risk. Not only can fine mapping help identify new genes that are causally linked to disease but this approach can also help exclude regions and SNPs that are unlikely to impact disease risk. New fine mapping data from the Wellcome Trust Case Control Consortium have moved the field forward on several levels by genotyping all known SNPs in 14 candidate regions, including those associated with type 2 diabetes, coronary artery disease, and Graves disease. In addition, the investigators apply Bayes theorem in their statistical analysis to determine posterior probabilities that the SNPs examined in the study are causally linked to variability in disease risk. Results of these efforts showed that in 3 of the 14 regions examined—including TCF7L2 for type 2 diabetes—a single SNP appeared to account for much of the probability linking SNPs with altered disease risk. In contrast, other results indicated that most SNPs in four other regions were not causally associated with disease risk. The new data provide a more granular view of promising susceptibility loci, and the accompanying statistical approaches appear to help distinguish promising regions from less promising ones, thereby allowing investigators to focus efforts in regions that are likely to be most fruitful. — Helaine E. Resnick, PhD, MPH

Wellcome Trust Case Control Consortium et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet 2012;44:1294–1301

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