Multiple LRRK2 variants modulate risk of Parkinson disease: a Chinese multicenter study

We and others found two polymorphic LRRK2 (leucine-rich repeat kinase 2) variants (rs34778348:G>A; p.G2385R and rs33949390:G>C; p.R1628P) associated with Parkinson disease (PD) among Chinese patients, but the common worldwide rs34637584:G>A; p.G2019S mutation, was absent. Focusing exclusively on Han Chinese, we first sequenced the coding regions in young onset and familial PD patients and identified 59 variants. We then examined these variants in 250 patients and 250 control subjects. Among the 17 polymorphic variants, five demonstrated different frequency in cases versus controls and were considered in a larger sample of 1,363 patients and 1,251 control subjects. The relative risk of an individual with both p.G2385R and p.R1628P is about 1.9, and this is reduced to 1.5-1.6 if the individual also carries rs7133914:G>C; p.R1398H or rs7308720:C>A: p.N551K. The risk of a carrier with p.R1628P is largely negated if the individual also carries p.R1398H or p.N551K. In dopaminergic neuronal lines, p.R1398H had significantly lower kinase activity, whereas p.G2385R and p.R1628P showed higher kinase activity than wild type. We provided the first evidence that multiple LRRK2 variants exert an individual effect and together modulate the risk of PD among Chinese.

Serum tests, liver stiffness and artificial neural networks for diagnosing cirrhosis and portal hypertension

BACKGROUND The diagnostic performance of biochemical scores and artificial neural network models for portal hypertension and cirrhosis is not well established. AIMS To assess diagnostic accuracy of six serum scores, artificial neural networks and liver stiffness measured by transient elastography, for diagnosing cirrhosis, clinically significant portal hypertension and oesophageal varices. METHODS 202 consecutive compensated patients requiring liver biopsy and hepatic venous pressure gradient measurement were included. Several serum tests (alone and combined into scores) and liver stiffness were measured. Artificial neural networks containing or not liver stiffness as input variable were also created. RESULTS The best non-invasive method for diagnosing cirrhosis, portal hypertension and oesophageal varices was liver stiffness (C-statistics=0.93, 0.94, and 0.90, respectively). Among serum tests/scores the best for diagnosing cirrhosis and portal hypertension and oesophageal varices were, respectively, Fibrosis-4, and Lok score. Artificial neural networks including liver stiffness had high diagnostic performance for cirrhosis, portal hypertension and oesophageal varices (accuracy>80%), but were not statistically superior to liver stiffness alone. CONCLUSIONS Liver stiffness was the best non-invasive method to assess the presence of cirrhosis, portal hypertension and oesophageal varices. The use of artificial neural networks integrating different non-invasive tests did not increase the diagnostic accuracy of liver stiffness alone.