Causal relations of health indices inferred statistically using the DirectLiNGAM

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012–2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than 104. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes

Role of exosomes as a proinflammatory mediator in the development of EBV-associated lymphoma

Epstein-Barr virus (EBV) causes various diseases in the elderly, including B-cell lymphoma such as Hodgkin's lymphoma and diffuse large B-cell lymphoma. Here, we show that EBV acts in trans on noninfected macrophages in the tumor through exosome secretion and augments the development of lymphomas. In a humanized mouse model, the different formation of lymphoproliferative disease (LPD) between 2 EBV strains (Akata and B95-8) was evident. Furthermore, injection of Akata-derived exosomes affected LPD severity, possibly through the regulation of macrophage phenotype in vivo. Exosomes collected from Akata-lymphoblastoid cell lines reportedly contain EBV-derived noncoding RNAs such as BamHI fragment A rightward transcript (BART) micro-RNAs (miRNAs) and EBVencoded RNA.We focused on the exosome-mediated delivery of BART miRNAs. In vitro, BART miRNAs could induce the immune regulatory phenotype in macrophages characterized by the gene expressions of interleukin 10, tumor necrosis factor-a, and arginase 1, suggesting the immune regulatory role of BART miRNAs.The expression level of an EBV-encoded miRNA was strongly linked to the clinical outcomes in elderly patients with diffuse large B-cell lymphoma.These results implicate BART miRNAs as 1 of the factors regulating the severity of lymphoproliferative disease and as a diagnostic marker for EBV1 B-cell lymphoma. (Blood. 2018;131(23):2552-2567)

CNVs in Three Psychiatric Disorders

BACKGROUND: We aimed to determine the similarities and differences in the roles of genic and regulatory copy number variations (CNVs) in bipolar disorder (BD), schizophrenia (SCZ), and autism spectrum disorder (ASD). METHODS: Based on high-resolution CNV data from 8708 Japanese samples, we performed to our knowledge the largest cross-disorder analysis of genic and regulatory CNVs in BD, SCZ, and ASD. RESULTS: In genic CNVs, we found an increased burden of smaller (500 kb) exonic CNVs in SCZ/ASD. Pathogenic CNVs linked to neurodevelopmental disorders were significantly associated with the risk for each disorder, but BD and SCZ/ASD differed in terms of the effect size (smaller in BD) and subtype distribution of CNVs linked to neurodevelopmental disorders. We identified 3 synaptic genes (DLG2, PCDH15, and ASTN2) as risk factors for BD. Whereas gene set analysis showed that BD-associated pathways were restricted to chromatin biology, SCZ and ASD involved more extensive and similar pathways. Nevertheless, a correlation analysis of gene set results indicated weak but significant pathway similarities between BD and SCZ or ASD (r = 0.25–0.31). In SCZ and ASD, but not BD, CNVs were significantly enriched in enhancers and promoters in brain tissue. CONCLUSIONS: BD and SCZ/ASD differ in terms of CNV burden, characteristics of CNVs linked to neurodevelopmental disorders, and regulatory CNVs. On the other hand, they have shared molecular mechanisms, including chromatin biology. The BD risk genes identified here could provide insight into the pathogenesis of BD

Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups.

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012-2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses