Genetic Variant in HK1 Is Associated With a Proanemic State and A1C but Not Other Glycemic Control–Related Traits

OBJECTIVE A1C is widely considered the gold standard for monitoring effective blood glucose levels. Recently, a genome-wide association study reported an association between A1C and rs7072268 within HK1 (encoding hexokinase 1), which catalyzes the first step of glycolysis. HK1 deficiency in erythrocytes (red blood cells [RBCs]) causes severe nonspherocytic hemolytic anemia in both humans and mice. RESEARCH DESIGN AND METHODS The contribution of rs7072268 to A1C and the RBC-related traits was assessed in 6,953 nondiabetic European participants. We additionally analyzed the association with hematologic traits in 5,229 nondiabetic European individuals (in whom A1C was not measured) and 1,924 diabetic patients. Glucose control–related markers other than A1C were analyzed in 18,694 nondiabetic European individuals. A type 2 diabetes case-control study included 7,447 French diabetic patients. RESULTS Our study confirms a strong association between the rs7072268–T allele and increased A1C (β = 0.029%; P = 2.22 × 10−7). Surprisingly, despite adequate study power, rs7072268 showed no association with any other markers of glucose control (fasting- and 2-h post-OGTT–related parameters, n = 18,694). In contrast, rs7072268–T allele decreases hemoglobin levels (n = 13,416; β = −0.054 g/dl; P = 3.74 × 10−6) and hematocrit (n = 11,492; β = −0.13%; P = 2.26 × 10−4), suggesting a proanemic effect. The T allele also increases risk for anemia (836 cases; odds ratio 1.13; P = 0.018). CONCLUSIONS HK1 variation, although strongly associated with A1C, does not seem to be involved in blood glucose control. Since HK1 rs7072268 is associated with reduced hemoglobin levels and favors anemia, we propose that HK1 may influence A1C levels through its anemic effect or its effect on glucose metabolism in RBCs. These findings may have implications for type 2 diabetes diagnosis and clinical management because anemia is a frequent complication of the diabetes state

Comparison of accuracy of fibrosis degree classifications by liver biopsy and non-invasive tests in chronic hepatitis C

<p>Abstract</p> <p>Background</p> <p>Non-invasive tests have been constructed and evaluated mainly for binary diagnoses such as significant fibrosis. Recently, detailed fibrosis classifications for several non-invasive tests have been developed, but their accuracy has not been thoroughly evaluated in comparison to liver biopsy, especially in clinical practice and for Fibroscan. Therefore, the main aim of the present study was to evaluate the accuracy of detailed fibrosis classifications available for non-invasive tests and liver biopsy. The secondary aim was to validate these accuracies in independent populations.</p> <p>Methods</p> <p>Four HCV populations provided 2,068 patients with liver biopsy, four different pathologist skill-levels and non-invasive tests. Results were expressed as percentages of correctly classified patients.</p> <p>Results</p> <p>In population #1 including 205 patients and comparing liver biopsy (reference: consensus reading by two experts) and blood tests, Metavir fibrosis (F_M) stage accuracy was 64.4% in local pathologists vs. 82.2% (p < 10^-3) in single expert pathologist. Significant discrepancy (≥ 2F_Mvs reference histological result) rates were: Fibrotest: 17.2%, FibroMeter^2G: 5.6%, local pathologists: 4.9%, FibroMeter^3G: 0.5%, expert pathologist: 0% (p < 10^-3). In population #2 including 1,056 patients and comparing blood tests, the discrepancy scores, taking into account the error magnitude, of detailed fibrosis classification were significantly different between FibroMeter^2G(0.30 ± 0.55) and FibroMeter^3G(0.14 ± 0.37, p < 10^-3) or Fibrotest (0.84 ± 0.80, p < 10^-3). In population #3 (and #4) including 458 (359) patients and comparing blood tests and Fibroscan, accuracies of detailed fibrosis classification were, respectively: Fibrotest: 42.5% (33.5%), Fibroscan: 64.9% (50.7%), FibroMeter^2G: 68.7% (68.2%), FibroMeter^3G: 77.1% (83.4%), p < 10^-3(p < 10^-3). Significant discrepancy (≥ 2 F_M) rates were, respectively: Fibrotest: 21.3% (22.2%), Fibroscan: 12.9% (12.3%), FibroMeter^2G: 5.7% (6.0%), FibroMeter^3G: 0.9% (0.9%), p < 10^-3(p < 10^-3).</p> <p>Conclusions</p> <p>The accuracy in detailed fibrosis classification of the best-performing blood test outperforms liver biopsy read by a local pathologist, i.e., in clinical practice; however, the classification precision is apparently lesser. This detailed classification accuracy is much lower than that of significant fibrosis with Fibroscan and even Fibrotest but higher with FibroMeter^3G. FibroMeter classification accuracy was significantly higher than those of other non-invasive tests. Finally, for hepatitis C evaluation in clinical practice, fibrosis degree can be evaluated using an accurate blood test.</p

Qualitative simulation and validation of complex hybrid systems

International audienceComplex industrial systems need extensive validation and verification. Methods for this are well advanced in case of discrete systems. However, for hybrid systems that combine discrete and continuous aspects, they are not as well developed. To deal with this, qualitative simulation can be used, based on the principle of discretization by identifying domains of variation of continuous variables and tracking the evolution of these variables. A system can be discretized by representing its continuous parts, which are described by differential equations. When these are coupled with the discrete parts of the system, a fully discrete global model is obtained, on which formal techniques can be applied for the validation process. If the differential equations cannot be expressed clearly, it is necessary to establish a qualitative model describing the laws of evolution of continuous variables. We defined and tested a novel methodology that represents variations of continuous variables and the causal links between them to obtain mappings of system behaviors that are suitable for validation

Towards Slicing Communicating Extended Automata

Abstract. Slicing is a well-established program analysis technique that has applications in debugging, program understanding and model reduction. This paper presents an approach to slicing formal specifications based on communicating extended automata.