Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence

Background

State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity.

Methods

Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods.

Results

Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%–in contrast, most states were below 30% in CDC maps.

Conclusions

Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.     

Evolution of E. coli on [U-13C]Glucose Reveals a Negligible Isotopic Influence on Metabolism and Physiology

¹³C-Metabolic flux analysis (¹³C-MFA) traditionally assumes that kinetic isotope effects from isotopically labeled compounds do not appreciably alter cellular growth or metabolism, despite indications that some biochemical reactions can be non-negligibly impacted. Here, populations of Escherichia coli were adaptively evolved for ~1000 generations on uniformly labeled ¹³C-glucose, a commonly used isotope for ¹³C-MFA. Phenotypic characterization of these evolved strains revealed ~40% increases in growth rate, with no significant difference in fitness when grown on either labeled (¹³C) or unlabeled (¹²C) glucose. The evolved strains displayed decreased biomass yields, increased glucose and oxygen uptake, and increased acetate production, mimicking what is observed after adaptive evolution on unlabeled glucose. Furthermore, full genome re-sequencing revealed that the key genetic changes underlying these phenotypic alterations were essentially the same as those acquired during adaptive evolution on unlabeled glucose. Additionally, glucose competition experiments demonstrated that the wild-type exhibits no isotopic preference for unlabeled glucose, and the evolved strains have no preference for labeled glucose. Overall, the results of this study indicate that there are no significant differences between ¹²C and ¹³C-glucose as a carbon source for E. coli growth.     

Biomechanical Characteristics of Osteoporotic Fracture Healing in Ovariectomized Rats: A Systematic Review

Biomechanical tests are widely used in animal studies on osteoporotic fracture healing. However, the biomechanical recovery process is still unknown, leading to difficulty in choosing time points for biomechanical tests and in correctly assessing osteoporotic fracture healing. To determine the biomechanical recovery process during osteoporotic fracture healing, studies on osteoporotic femur fracture healing with biomechanical tests in ovariectomized rat (OVX) models were collected from PUBMED, EMBASE, and Chinese databases. Quadratic curves of fracture healing time and maximum load were fitted with data from the analyzed studies. In the fitted curve for normal fractures, the predicted maximum load was 145.56 N, and the fracture healing time was 88.0 d. In the fitted curve for osteoporotic fractures, the predicted maximum load was 122.30 N, and the fracture healing time was 95.2 d. The maximum load of fractured femurs in OVX rats was also lower than that in sham rats at day 84 post-fracture (D84 PF). The fracture healing time was prolonged and maximum load at D84 PF decreased in OVX rats with closed fractures. The maximum load of Wister rats was higher than that of Sprague-Dawley (SD) rats, but the fracture healing time of SD and Wister rats was similar. Osteoporotic fracture healing was delayed in rats that were < = 12 weeks old when ovariectomized, and at D84 PF, the maximum load of rats < = 12 weeks old at ovariectomy was lower than that of rats >12 weeks old at ovariectomy. There was no significant difference in maximum load at D84 PF between rats with an osteoporosis modeling time <12 weeks and > = 12 weeks. In conclusion, fracture healing was delayed and biomechanical property decreased by osteoporosis. Time points around D95.2 PF should be considered for biomechanical tests of osteoporotic femur fracture healing in OVX rat models. Osteoporotic fracture healing in OVX rats was affected by the fracture type but not by the strain of the rat.     

Using simulations to evaluate Mantel‐based methods for assessing landscape resistance to gene flow

Mantel‐based tests have been the primary analytical methods for understanding how landscape features influence observed spatial genetic structure. Simulation studies examining Mantel‐based approaches have highlighted major challenges associated with the use of such tests and fueled debate on when the Mantel test is appropriate for landscape genetics studies. We aim to provide some clarity in this debate using spatially explicit, individual‐based, genetic simulations to examine the effects of the following on the performance of Mantel‐based methods: (1) landscape configuration, (2) spatial genetic nonequilibrium, (3) nonlinear relationships between genetic and cost distances, and (4) correlation among cost distances derived from competing resistance models. Under most conditions, Mantel‐based methods performed poorly. Causal modeling identified the true model only 22% of the time. Using relative support and simple Mantel r values boosted performance to approximately 50%. Across all methods, performance increased when landscapes were more fragmented, spatial genetic equilibrium was reached, and the relationship between cost distance and genetic distance was linearized. Performance depended on cost distance correlations among resistance models rather than cell‐wise resistance correlations. Given these results, we suggest that the use of Mantel tests with linearized relationships is appropriate for discriminating among resistance models that have cost distance correlations <0.85 with each other for causal modeling, or <0.95 for relative support or simple Mantel r. Because most alternative parameterizations of resistance for the same landscape variable will result in highly correlated cost distances, the use of Mantel test‐based methods to fine‐tune resistance values will often not be effective.     

PCI-24781 can improve in vitro and in vivo developmental capacity of pig somatic cell nuclear transfer embryos

Objective

To examine the effect of PCI-24781 (abexinostat) on the blastocyst formation rate in pig somatic cell nuclear transferred (SCNT) embryos and acetylation levels of the histone H3 lysine 9 and histone H4 lysine 12.

Results

Treatment with 0.5 nM PCI-24781 for 6 h significantly improved the development of cloned embryos, in comparison to the control group (25.3 vs. 10.5 %, P < 0.05). Furthermore, PCI-24781 treatment led to elevated acetylation of H3K9 and H4K12. TUNEL assay and Hoechst 33342 staining revealed that the percentage of apoptotic cells in blastocysts was significantly lower in PCI-24781-treated SCNT embryos than in untreated embryos. Also, PCI-24781-treated embryos were transferred into three surrogate sows, one of whom became pregnant and two fetuses developed.

Conclusion

PCI-24781 improves nuclear reprogramming and the developmental potential of pig SCNT embryos.