Nutritional Status in Liver Cirrhosis

The metabolism of many nutritional elements (carbohydrate, protein, fat, vitamins, and minerals) is gradually disturbed with progressive chronic liver diseases. In particular, protein‐energy malnutrition (PEM) is known as the most characteristic manifestation of liver cirrhosis (LC) and is closely related to its prognosis. Recently, while sarcopenia (loss of muscle mass and strength or physical performance) has been discussed as an independent factor associated with prognosis in patients with LC, obesity and insulin resistance in patients with LC also contribute to carcinogenesis in LC. Deficiencies of zinc and carnitine are involved in the malnutrition in LC and are associated with hyperammonemia, which is related to the pathogenesis of hepatic encephalopathy. Because the nutritional and metabolic disturbances in LC are fundamentally influenced by many factors, such as the severity of liver damage, the existence of portal‐systemic shunting, and inflammation, proper nutritional assessment is necessary for the nutritional management of patients with LC

Genetic Predisposition to Ischemic Stroke

Background and Purpose—The prediction of genetic predispositions to ischemic stroke (IS) may allow the identification of individuals at elevated risk and thereby prevent IS in clinical practice. Previously developed weighted multilocus genetic risk scores showed limited predictive ability for IS. Here, we investigated the predictive ability of a newer method, polygenic risk score (polyGRS), based on the idea that a few strong signals, as well as several weaker signals, can be collectively informative to determine IS risk.Methods—We genotyped 13 214 Japanese individuals with IS and 26 470 controls (derivation samples) and generated both multilocus genetic risk scores and polyGRS, using the same derivation data set. The predictive abilities of each scoring system were then assessed using 2 independent sets of Japanese samples (KyushuU and JPJM data sets).Results—In both validation data sets, polyGRS was shown to be significantly associated with IS, but weighted multilocus genetic risk scores was not. Comparing the highest with the lowest polyGRS quintile, the odds ratios for IS were 1.75 (95% confidence interval, 1.33–2.31) and 1.99 (95% confidence interval, 1.19–3.33) in the KyushuU and JPJM samples, respectively. Using the KyushuU samples, the addition of polyGRS to a nongenetic risk model resulted in a significant improvement of the predictive ability (net reclassification improvement=0.151; P<0.001).Conclusions—The polyGRS was shown to be superior to weighted multilocus genetic risk scores as an IS prediction model. Thus, together with the nongenetic risk factors, polyGRS will provide valuable information for individual risk assessment and management of modifiable risk factors