The Impact of PNPLA3 rs738409 Genetic Polymorphism and Weight Gain ≥10 kg after Age 20 on Non-Alcoholic Fatty Liver Disease in Non-Obese Japanese Individuals.

Non-alcoholic fatty liver disease (NAFLD) in non-obese individuals is inadequately elucidated. We aim to investigate the impact of known genetic polymorphisms on NAFLD and the interaction between genetic risks and weight gain on NAFLD in obese and non-obese Japanese individuals. A total of 1164 participants who received health checkups were included. Participants with excessive alcohol consumption, with viral hepatitis or other inappropriate cases were excluded. Fatty liver was diagnosed by ultrasonography. Participants with a body mass index (BMI) of <18.5 kg/m2, 18.5-22.9 kg/m2, 23.0-24.9 kg/m2 and ≥25 kg/m2 were classified underweight, normal weight, overweight and obese, respectively. Self-administered questionnaire for lifestyle was assessed and a total of 8 previously reported genetic polymorphisms were chosen and examined. In all, 824 subjects were enrolled. The overall prevalence of NAFLD was 33.0%: 0% in underweight, 15.3% in normal weight, 41.1% in overweight and 71.7% in obese individuals. The prevalence of NAFLD is more affected by the G allele of patatin-like phospholipase domain-containing protein 3 (PNPLA3) rs738409 in normal weight (odds ratio (OR) 3.52; 95%-CI: 1.42-8.71; P = 0.0063) and in overweight individuals (OR 2.60; 95%-CI: 1.14-5.91; P = 0.0225) than in obese individuals (not significant). Moreover, the G allele of PNPLA3 rs738409 and weight gain ≥10 kg after age 20 had a joint effect on the risk of NAFLD in the normal weight (OR 12.00; 95% CI: 3.71-38.79; P = 3.3×10-5) and the overweight individuals (OR 13.40; 95% CI: 2.92-61.36; P = 0.0008). The G allele of PNPLA3 rs738409 is a prominent risk factor for NAFLD and the interaction between the PNPLA3 rs738409 and weight gain ≥10 kg after age 20 plays a crucial role in the pathogenesis of NAFLD, especially in non-obese Japanese individuals

Clinical Implications of Chemokines in Acute and Chronic Hepatitis C Virus Infection

Hepatitis C virus (HCV), a non-cytopathic positive-stranded RNA virus, is one of the most common causes of chronic liver diseases such as chronic hepatitis, liver cirrhosis and hepatocellular carcinoma. Upon HCV infection, the majority of patients fail to clear the virus and progress to chronic hepatitis C. Chemokines are small chemotactic cytokines that direct the recruitment of immune cells and coordinate immune responses upon viral infection. Chemokine production during acute HCV infection contributes to the recruitment of immune cells with antiviral effector functions and subsequent viral clearance. In chronic HCV infection, however, continuous production of chemokines due to persistent viral replication might result in incessant recruitment of inflammatory cells to the liver, giving rise to persistence of chronic inflammation and liver injury. In this review, we will summarize the roles of chemokines in acute and chronic settings of HCV infection and the clinical relevance of chemokines in the treatment of hepatitis C

Lymphocyte recruitment and homing to the liver in primary biliary cirrhosis and primary sclerosing cholangitis

The mechanisms operating in lymphocyte recruitment and homing to liver are reviewed. A literature review was performed on primary biliary cirrhosis (PBC), progressive sclerosing cholangitis (PSC), and homing mechanisms; a total of 130 papers were selected for discussion. Available data suggest that in addition to a specific role for CCL25 in PSC, the CC chemokines CCL21 and CCL28 and the CXC chemokines CXCL9 and CXCL10 are involved in the recruitment of T lymphocytes into the portal tract in PBC and PSC. Once entering the liver, lymphocytes localize to bile duct and retain by the combinatorial or sequential action of CXCL12, CXCL16, CX3CL1, and CCL28 and possibly CXCL9 and CXCL10. The relative importance of these chemokines in the recruitment or the retention of lymphocytes around the bile ducts remains unclear. The available data remain limited but underscore the importance of recruitment and homing

Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis

A considerable amount of health record (HR) data has been stored due to recent advances in the digitalization of medical systems. However, it is not always easy to analyze HR data, particularly when the number of persons with a target disease is too small in comparison with the population. This situation is called the imbalanced data problem. Over-sampling and under-sampling are two approaches for redressing an imbalance between minority and majority examples, which can be combined into ensemble algorithms. However, these approaches do not function when the absolute number of minority examples is small, which is called the extremely imbalanced and small minority (EISM) data problem. The present work proposes a new algorithm called boosting combined with heuristic under-sampling and distribution-based sampling (HUSDOS-Boost) to solve the EISM data problem. To make an artificially balanced dataset from the original imbalanced datasets, HUSDOS-Boost uses both under-sampling and over-sampling to eliminate redundant majority examples based on prior boosting results and to generate artificial minority examples by following the minority class distribution. The performance and characteristics of HUSDOS-Boost were evaluated through application to eight imbalanced datasets. In addition, the algorithm was applied to original clinical HR data to detect patients with stomach cancer. These results showed that HUSDOS-Boost outperformed current imbalanced data handling methods, particularly when the data are EISM. Thus, the proposed HUSDOS-Boost is a useful methodology of HR data analysis

Medical checkup data analysis method based on LiNGAM and its application to nonalcoholic fatty liver disease.

Although medical checkup data would be useful for identifying unknown factors of disease progression, a causal relationship between checkup items should be taken into account for precise analysis. Missing values in medical checkup data must be appropriately imputed because checkup items vary from person to person, and items that have not been tested include missing values. In addition, the patients with target diseases or disorders are small in comparison with the total number of persons recorded in the data, which means medical checkup data is an imbalanced data analysis. We propose a new method for analyzing the causal relationship in medical checkup data to discover disease progression factors based on a linear non-Gaussian acyclic model (LiNGAM), a machine learning technique for causal inference. In the proposed method, specific regression coefficients calculated through LiNGAM were compared to estimate the causal strength of the checkup items on disease progression, which is referred to as LiNGAM-beta. We also propose an analysis framework consisting of LiNGAM-beta, collaborative filtering (CF), and a sampling approach for causal inference of medical checkup data. CF and the sampling approach are useful for missing value imputation and balancing of the data distribution. We applied the proposed analysis framework to medical checkup data for identifying factors of Nonalcoholic fatty liver disease (NAFLD) development. The checkup items related to metabolic syndrome and age showed high causal effects on NAFLD severity. The level of blood urea nitrogen (BUN) would have a negative effect on NAFLD severity. Snoring frequency, which is associated with obstructive sleep apnea, affected NAFLD severity, particularly in the male group. Sleep duration also affected NAFLD severity in persons over fifty years old. These analysis results are consistent with previous reports about the causes of NAFLD; for example, NAFLD and metabolic syndrome are mutual and bi-directionally related, and BUN has a negative effect on NAFLD progression. Thus, our analysis result is plausible. The proposed analysis framework including LiNGAM-beta can be applied to various medical checkup data and will contribute to discovering unknown disease factors

Medical checkup data analysis method based on LiNGAM and its application to nonalcoholic fatty liver disease.

Although medical checkup data would be useful for identifying unknown factors of disease progression, a causal relationship between checkup items should be taken into account for precise analysis. Missing values in medical checkup data must be appropriately imputed because checkup items vary from person to person, and items that have not been tested include missing values. In addition, the patients with target diseases or disorders are small in comparison with the total number of persons recorded in the data, which means medical checkup data is an imbalanced data analysis. We propose a new method for analyzing the causal relationship in medical checkup data to discover disease progression factors based on a linear non-Gaussian acyclic model (LiNGAM), a machine learning technique for causal inference. In the proposed method, specific regression coefficients calculated through LiNGAM were compared to estimate the causal strength of the checkup items on disease progression, which is referred to as LiNGAM-beta. We also propose an analysis framework consisting of LiNGAM-beta, collaborative filtering (CF), and a sampling approach for causal inference of medical checkup data. CF and the sampling approach are useful for missing value imputation and balancing of the data distribution. We applied the proposed analysis framework to medical checkup data for identifying factors of Nonalcoholic fatty liver disease (NAFLD) development. The checkup items related to metabolic syndrome and age showed high causal effects on NAFLD severity. The level of blood urea nitrogen (BUN) would have a negative effect on NAFLD severity. Snoring frequency, which is associated with obstructive sleep apnea, affected NAFLD severity, particularly in the male group. Sleep duration also affected NAFLD severity in persons over fifty years old. These analysis results are consistent with previous reports about the causes of NAFLD; for example, NAFLD and metabolic syndrome are mutual and bi-directionally related, and BUN has a negative effect on NAFLD progression. Thus, our analysis result is plausible. The proposed analysis framework including LiNGAM-beta can be applied to various medical checkup data and will contribute to discovering unknown disease factors