Subgrouping time-dependent prescribing patterns of first-onset major depressive episodes by psychotropics dissection.

BACKGROUND: Subgrouping patients with major depressive disorder is a promising solution for the issue of heterogeneity. However, the link between available subtypes and distinct pathological mechanisms is weak and yields disappointing results in clinical application. AIM: To develop a novel approach for classification of patients with time-dependent prescription patterns at first onset in real-world settings. METHODS: Drug-naive patients experiencing their first major depressive episode (n = 105) participated in this study. Psychotropic agents prescribed in the first 24 mo following disease onset were recorded monthly and categorized as antidepressants, augmentation agents, and hypnosedatives. Monthly cumulative doses of agents in each category were converted into relevant equivalents. Four parameters were used to summarize the time-dependent prescription patterns for each psychotropic load: Stability, amount, frequency, and the time trend of monthly prescriptions. A K-means cluster analysis was used to derive subgroups of participants based on these input parameters of psychotropic agents across 24 mo. Clinical validity of the resulting data-driven clusters was compared using relevant severity indicators. RESULTS: Four distinct clusters were derived from K-means analysis, which matches experts' consent: "Short-term antidepressants use", "long-term antidepressants use", "long-term antidepressants and sedatives use", and "long-term antidepressants, sedatives, and augmentation use". At the first 2 years of disease course, the four clusters differed on the number of antidepressants used at adequate dosage and duration, frequency of outpatient service use, and number of psychiatric admissions. After the first 2 years following disease onset, depression severity was differed in the four subgroups. CONCLUSION: Our findings suggested a new approach to optimize the subgrouping of patients with major depressive disorder, which may assist future etiological and treatment response studies.

Identification of regulatory SNPs associated with genetic modifications in lung adenocarcinoma.

BACKGROUND: Although much research effort has been devoted to elucidating lung cancer, the molecular mechanism of tumorigenesis still remains unclear. A major challenge to improve the understanding of lung cancer is the difficulty of identifying reproducible differentially expressed genes across independent studies, due to their low consistency. To enhance the reproducibility of the findings, an integrated analysis was performed to identify regulatory SNPs. Thirty-two pairs of tumor and adjacent normal lung tissue specimens were analyzed using Affymetrix U133plus2.0, Affymetrix SNP 6.0, and Illumina Infinium Methylation microarrays. Copy number variations (CNVs) and methylation alterations were analyzed and paired t-tests were used to identify differentially expressed genes. RESULTS: A total of 505 differentially expressed genes were identified, and their dysregulated patterns moderately correlated with CNVs and methylation alterations based on the hierarchical clustering analysis. Subsequently, three statistical approaches were performed to explore regulatory SNPs, which revealed that the genotypes of 551 and 66 SNPs were associated with CNV and changes in methylation, respectively. Among them, downstream transcriptional dysregulation was observed in 9 SNPs for CNVs and 4 SNPs for methylation alterations. CONCLUSIONS: In summary, these identified SNPs concurrently showed the same direction of gene expression changes with genetic modifications, suggesting their pivotal roles in the genome for non-smoking women with lung adenocarcinoma.

SNP rs10248565 in HDAC9 as a novel genomic aberration biomarker of lung adenocarcinoma in non-smoking women.

BACKGROUND: Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Although cigarette smoking is the primary risk factor for lung cancer, only 7% of female lung cancer patients in Taiwan have a history of smoking. Since cancer results from progressive accumulation of genetic aberrations, genomic rearrangements may be early events in carcinogenesis. RESULTS: In order to identify biomarkers of early-stage adenocarcinoma, the genome-wide DNA aberrations of 60 pairs of lung adenocarcinoma and adjacent normal lung tissue in non-smoking women were examined using Affymetrix Genome-Wide Human SNP 6.0 arrays. Common copy number variation (CNV) regions were identified by ≥30% of patients with copy number beyond 2 ± 0.5 of copy numbers for each single nucleotide polymorphism (SNP) and at least 100 continuous SNP variant loci. SNPs associated with lung adenocarcinoma were identified by McNemar's test. Loss of heterozygosity (LOH) SNPs were identified in ≥18% of patients with LOH in the locus. Aberration of SNP rs10248565 at HDAC9 in chromosome 7p21.1 was identified from concurrent analyses of CNVs, SNPs, and LOH. CONCLUSION: The results elucidate the genetic etiology of lung adenocarcinoma by demonstrating that SNP rs10248565 may be a potential biomarker of cancer susceptibility.

Spontaneous seroclearance of hepatitis B seromarkers and subsequent risk of hepatocellular carcinoma.

BACKGROUND AND AIMS: The associations between long-term risk of hepatocellular carcinoma (HCC) and spontaneous seroclearance of HBV e antigen (HBeAg), HBV DNA and HBV surface antigen (HBsAg) have never been examined by a prospective study using serially measured seromarkers. This study aimed to assess the importance of spontaneous HBeAg, HBV DNA and HBsAg seroclearance in the prediction of HCC risk. METHODS: This study included 2946 HBsAg seropositive individuals who were seronegative for antibodies against HCV and free of liver cirrhosis. Serial serum samples collected at study entry and follow-up health examinations were tested for HBeAg, HBV DNA and HBsAg. Cox proportional hazards models were used to calculate the HRs of developing HCC after seroclearance of HBV markers. RESULTS: The HR (95% CI) of developing HCC after seroclearance of HBeAg, HBV DNA and HBsAg during follow-up was 0.63 (0.38 to 1.05), 0.24 (0.11 to 0.57) and 0.18 (0.09 to 0.38), respectively, after adjustment for age, gender and serum level of alanine aminotransferase at study entry. High HBV DNA levels at the seroclearance of HBeAg (mean±SD, 4.35±1.64 log10 IU/mL) may explain the non-significant association between HBeAg seroclearance and HCC risk. Among HBeAg seronegative participants with detectable serum HBV DNA at study entry, the lifetime (30-75-years-old) cumulative incidence of HCC was 4.0%, 6.6% and 14.2%, respectively, for those with seroclearance of both HBV DNA and HBsAg, seroclearance of HBV DNA only, and seroclearance of neither. CONCLUSIONS: Spontaneous seroclearance of HBV DNA and HBsAg are important predictors of reduced HCC risk.

A stochastic inference of de novo CNV detection and association test in multiplex schizophrenia families.

The copy number variation (CNV) is a type of genetic variation in the genome. It is measured based on signal intensity measures and can be assessed repeatedly to reduce the uncertainty in PCR-based typing. Studies have shown that CNVs may lead to phenotypic variation and modification of disease expression. Various challenges exist, however, in the exploration of CNV-disease association. Here we construct latent variables to infer the discrete CNV values and to estimate the probability of mutations. In addition, we propose to pool rare variants to increase the statistical power and we conduct family studies to mitigate the computational burden in determining the composition of CNVs on each chromosome. To explore in a stochastic sense the association between the collapsing CNV variants and disease status, we utilize a Bayesian hierarchical model incorporating the mutation parameters. This model assigns integers in a probabilistic sense to the quantitatively measured copy numbers, and is able to test simultaneously the association for all variants of interest in a regression framework. This integrative model can account for the uncertainty in copy number assignment and differentiate if the variation was de novo or inherited on the basis of posterior probabilities. For family studies, this model can accommodate the dependence within family members and among repeated CNV data. Moreover, the Mendelian rule can be assumed under this model and yet the genetic variation, including de novo and inherited variation, can still be included and quantified directly for each individual. Finally, simulation studies show that this model has high true positive and low false positive rates in the detection of de novo mutation.

Inference of cross-level interaction between genes and contextual factors in a matched case-control metabolic syndrome study: a Bayesian approach.

Genes, environment, and the interaction between them are each known to play an important role in the risk for developing complex diseases such as metabolic syndrome. For environmental factors, most studies focused on the measurements observed at the individual level, and therefore can only consider the gene-environment interaction at the same individual scale. Indeed the group-level (called contextual) environmental variables, such as community factors and the degree of local area development, may modify the genetic effect as well. To examine such cross-level interaction between genes and contextual factors, a flexible statistical model quantifying the variability of the genetic effects across different categories of the contextual variable is in need. With a Bayesian generalized linear mixed-effects model with an unconditional likelihood, we investigate whether the individual genetic effect is modified by the group-level residential environment factor in a matched case-control metabolic syndrome study. Such cross-level interaction is evaluated by examining the heterogeneity in allelic effects under various contextual categories, based on posterior samples from Markov chain Monte Carlo methods. The Bayesian analysis indicates that the effect of rs1801282 on metabolic syndrome development is modified by the contextual environmental factor. That is, even among individuals with the same genetic component of PPARG_Pro12Ala, living in a residential area with low availability of exercise facilities may result in higher risk. The modification of the group-level environment factors on the individual genetic attributes can be essential, and this Bayesian model is able to provide a quantitative assessment for such cross-level interaction. The Bayesian inference based on the full likelihood is flexible with any phenotype, and easy to implement computationally. This model has a wide applicability and may help unravel the complexity in development of complex diseases.

A predictive scoring system for the seroclearance of HBsAg in HBeAg-seronegative chronic hepatitis B patients with genotype B or C infection.

BACKGROUND & AIMS: Seroclearance of hepatitis B surface antigen (HBsAg) is the most ideal end point in the treatment of chronic hepatitis B. This study develops a predictive scoring system to assess whether the addition of serum levels HBsAg may improve the predictability of HBsAg loss. METHODS: This study included 2491 untreated participants with genotype B or C HBV infection, who were HBsAg-seropositive, HBeAg-seronegative, anti-HCV-seronegative, and cirrhosis free at study entry. Regression coefficients of predictors in Cox Regression models were converted into integer scores for predicting HBsAg seroclearance. Predictive accuracy was assessed with area under the receiver operating characteristic curves (AUROC), and predictive accuracies of models with and without serum HBsAg levels were compared. RESULTS: Low serum levels of both HBsAg and HBV DNA were the strongest predictors of spontaneous HBsAg seroclearance. Compared to baseline serum HBsAg levels ≥1000 IU/ml, the multivariate adjusted rate ratio of spontaneous HBsAg seroclearance was 10.96 (7.92-15.16) for those with baseline serum HBsAg levels <100 IU/ml. The predictive ability of HBsAg levels was modified by HBV viral load, showing a weaker effect in those with higher viral loads, and the strongest effect among those with undetectable viral loads. The inclusion of serum HBsAg levels greatly improved the AUROC for predicting HBsAg seroclearance at the fifth (from 0.79 [0.787-0.792] to 0.89 [0.889-0.891]) and tenth year (from 0.73 [0.728-0.732] to 0.84 [0.839-0.841]) after study entry. CONCLUSIONS: Incorporated into an easy-to-use scoring system, HBV viral load and quantitative serum HBsAg levels can accurately predict HBsAg seroclearance.

Use of germline polymorphisms in predicting concurrent chemoradiotherapy response in esophageal cancer.

PURPOSE: To identify germline polymorphisms to predict concurrent chemoradiation therapy (CCRT) response in esophageal cancer patients. MATERIALS AND METHODS: A total of 139 esophageal cancer patients treated with CCRT (cisplatin-based chemotherapy combined with 40 Gy of irradiation) and subsequent esophagectomy were recruited at the National Taiwan University Hospital between 1997 and 2008. After excluding confounding factors (i.e., females and patients aged ≥70 years), 116 patients were enrolled to identify single nucleotide polymorphisms (SNPs) associated with specific CCRT responses. Genotyping arrays and mass spectrometry were used sequentially to determine germline polymorphisms from blood samples. These polymorphisms remain stable throughout disease progression, unlike somatic mutations from tumor tissues. Two-stage design and additive genetic models were adopted in this study. RESULTS: From the 26 SNPs identified in the first stage, 2 SNPs were found to be significantly associated with CCRT response in the second stage. Single nucleotide polymorphism rs16863886, located between SGPP2 and FARSB on chromosome 2q36.1, was significantly associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.62-10.30) under additive models. Single nucleotide polymorphism rs4954256, located in ZRANB3 on chromosome 2q21.3, was associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.57-10.87). The predictive accuracy for CCRT response was 71.59% with these two SNPs combined. CONCLUSIONS: This is the first study to identify germline polymorphisms with a high accuracy for predicting CCRT response in the treatment of esophageal cancer.

Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan.

Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients.

Changes in serum levels of HBV DNA and alanine aminotransferase determine risk for hepatocellular carcinoma.

BACKGROUND & AIMS: It is not clear whether risk for hepatocellular carcinoma can be accurately determined from long-term changes in serum levels of hepatitis B virus (HBV) DNA or alanine aminotransferase (ALT). METHODS: We measured serum levels of HBV DNA and ALT at enrollment and during follow-up analysis of 3160 participants in the REVEAL-HBV study. Development of hepatocellular carcinoma was determined from follow-up examinations and computerized linkage with National Cancer Registry and National Death Certification profiles. Multivariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression models. RESULTS: During 38,330 person-years of follow-up, 81 participants developed hepatocellular carcinoma (incidence rate, 211.3/100,000 person-years). The risk for hepatocellular carcinoma was only slightly higher for participants whose follow-up levels of HBV DNA spontaneously decreased to <10,000 copies/mL compared with those with baseline levels of HBV DNA<10,000 copies/mL (control group; HR, 2.25; 95% CI, 0.68-7.37). Compared with the control group, the HRs (95% CI) for long-term levels of HBV DNA that persisted at 10,000 to 100,000 copies/mL, decreased to/persisted at 100,000 to 1,000,000 copies/mL, or decreased to/persisted at 1,000,000 to 10,000,000 copies/mL were 3.12 (1.09-8.89), 8.85 (3.85-20.35), and 16.78 (7.33-38.39), respectively. A gradient in ALT level was significantly associated with hepatocellular carcinoma risk: from all low-normal, to ever high-normal, to transient abnormal, to persistent abnormal (Ptrend<.001). CONCLUSIONS: Long-term changes in serum levels of HBV DNA and ALT are independent predictors of risk for hepatocellular carcinoma. Regular monitoring of levels of HBV DNA and ALT is important in clinical management of chronic carriers of HBV.

Multilevel analysis of habitual physical activity and metabolic syndrome in Northern Taiwan.

OBJECTIVE: This study examines the relationship between the availability of public facilities for habitual physical activity in the community and metabolic syndrome in northern Taiwan, one of most densely populated countries in the world. METHODS: Subjects consisted of 14,658 participants (43.3% men and 56.7% women) ≥40 years old (mean=59.5) from 10 districts of Taoyuan County in a health check-up program in 2004-2005. Public facilities for habitual physical activity included school campuses and parks, and the density of such facilities was categorized into four levels. Multilevel logistic regression models were created to examine the effect on metabolic syndrome at both the individual and the contextual level using MLwiN software. RESULTS: The addition of the contextual variable to the model that included individual characteristics led to a further reduction of 7.2% in the variance. Using the facility density level I as the reference, the odds ratios (95% confidence interval) of metabolic syndrome for levels II, III, and IV were 0.87 (0.71-1.07), 0.87 (0.68-1.12), and 0.78 (0.61-0.99), respectively, with the trend test reaching significance. CONCLUSION: Greater availability of free facilities for habitual physical activity in a district was associated with a lower risk of metabolic syndrome among its residents.

Bayesian random effects for interrater and test-retest reliability with nested clinical observations.

OBJECTIVE: The assessment of inter- and intrarater reliability usually involves more than one level of nesting structures in the collected data, where repeated observations are made by multiple raters. Most approaches, however, are not designed to accommodate both inter- and intrarater reliability jointly, not to mention further difficulties arising when modeling with dichotomous responses. The multiple sources of dependence because of nesting structures and the existence of covariates can result in complexity in inference. STUDY DESIGN AND SETTING: We first establish the equivalence between correlation and kappa under common positive correlation models for multiple raters and then apply a Bayesian generalized linear mixed-effects model to interpret simultaneously both types of reproducibility through different annotations of similarity. In addition to marginal correlations, the correlated random effects among raters are adopted to infer similarity between raters, whereas the correlation for random time effects may contribute to test-retest reliability. RESULTS: This model accounts for individual covariates and random effects because of subjects, raters, and time, and it covers a wide variety of data structures and types. An application of endodontic radiographic examinations is illustrated. CONCLUSION: This Bayesian hierarchical correlation model offers a wide applicability, flexibility, and feasibility in modeling inter- and intrarater reliability together.

Extended Bayesian model averaging in generalized linear mixed models applied to schizophrenia family data.

The study of disease etiology and the search for susceptible genes of schizophrenia have attracted scientists' attention for decades. Many findings however are inconsistent, possibly due to the higher order interactions involving multi-dimensional genetic and environmental factors or due to the commingling of different ethnic groups. Several studies applied generalized linear mixed models (GLMMs) with family data to identify the genetic contribution to, and environmental influence on, schizophrenia, and to clarify the existence and sources of familial aggregation. Based on an extended Bayesian model averaging (EBMA) procedure, here we estimate the gene-gene (GG) and gene-environment (GE) interactions, and heritability of schizophrenia via variance components of random-effects in GLMMs. Our proposal takes into account the uncertainty in covariates and in genetic model structures, where each competing model includes environmental and genetic covariates, and GE and GG interactions. Simulation studies are conducted to compare the performance of the EBMA approach, permutation test procedure and GEE method. We also illustrate this approach with data from singleton and multiplex schizophrenia families. The results indicate that EBMA is a flexible and stable tool in exploring true candidate genes, and GE and GG interactions, after adjusting for explanatory variables and correlation structures within family members.

Impact of diabetes mellitus, hypertension, and coronary artery disease on tooth extraction after nonsurgical endodontic treatment.

INTRODUCTION: Limited prospective data are available on the long-term prognosis of teeth receiving nonsurgical root canal treatment (NSRCT) in patients with systemic diseases including diabetes mellitus (DM), hypertension (HT), and coronary artery disease (CAD). This prospective study aimed to elucidate the impact of systemic diseases on the risk of tooth extraction after NSRCT. METHODS: A total of 49,334 NSRCT teeth were randomly selected from databank in October 2003 and were followed for 2 years for tooth extraction after NSRCT. Cox proportional hazards model was used to estimate the risk of tooth extraction after NSRCT. RESULTS: Of the 49,334 teeth, 1592 (3.2%) were extracted during the 2-year follow-up period, yielding a 2-year tooth retention rate of 96.8%. We found that DM (hazard ratio [HR], 1.79), HT (HR, 1.75), and CAD (HR, 1.70) were significant risk factors for tooth extraction after NSRCT (all P values <.0001) in univariate Cox proportional analyses. After adjustment for age, gender, and tooth type in multivariate analyses, DM (HR, 1.29) and HT (HR, 1.18) remained as independent risk factors (both P values <.05). Simultaneous possession of 2 diseases of DM, HT, and CAD was a significant and robust predictor for an increased long-term risk of tooth extraction after NSRCT (P for trend <.001). CONCLUSIONS: An increased risk of tooth extraction after NSRCT is significantly associated with DM, HT, and CAD individually. Moreover, the constellation of systemic disease burden also manifests the importance in addition to other potential confounders.

Probabilistic daily ILI syndromic surveillance with a spatio-temporal Bayesian hierarchical model.

BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs.

A simple Bayesian mixture model with a hybrid procedure for genome-wide association studies.

Genome-wide association studies often face the undesirable result of either failing to detect any influential markers at all because of a stringent level for testing error corrections or encountering difficulty in quantifying the importance of markers by their P-values. Advocates of estimation procedures prefer to estimate the proportion of association rather than test significance to avoid overinterpretation. Here, we adopt a Bayesian hierarchical mixture model to estimate directly the proportion of influential markers, and then proceed to a selection procedure based on the Bayes factor (BF). This mixture model is able to accommodate different sources of dependence in the data through only a few parameters. Specifically, we focus on a standardized risk measure of unit variance so that fewer parameters are involved in inference. The expected value of this measure follows a mixture distribution with a mixing probability of association, and it is robust to minor allele frequencies. Furthermore, to select promising markers, we use the magnitude of the BF to represent the strength of evidence in support of the association between markers and disease. We demonstrate this procedure both with simulations and with SNP data from studies on rheumatoid arthritis, coronary artery disease, and Crohn's disease obtained from the Wellcome Trust Case-Control Consortium. This Bayesian procedure outperforms other existing methods in terms of accuracy, power, and computational efficiency. The R code that implements this method is available at http://homepage.ntu.edu.tw/~ckhsiao/Bmix/Bmix.htm.

A new regularized least squares support vector regression for gene selection.

BACKGROUND: Selection of influential genes with microarray data often faces the difficulties of a large number of genes and a relatively small group of subjects. In addition to the curse of dimensionality, many gene selection methods weight the contribution from each individual subject equally. This equal-contribution assumption cannot account for the possible dependence among subjects who associate similarly to the disease, and may restrict the selection of influential genes. RESULTS: A novel approach to gene selection is proposed based on kernel similarities and kernel weights. We do not assume uniformity for subject contribution. Weights are calculated via regularized least squares support vector regression (RLS-SVR) of class levels on kernel similarities and are used to weight subject contribution. The cumulative sum of weighted expression levels are next ranked to select responsible genes. These procedures also work for multiclass classification. We demonstrate this algorithm on acute leukemia, colon cancer, small, round blue cell tumors of childhood, breast cancer, and lung cancer studies, using kernel Fisher discriminant analysis and support vector machines as classifiers. Other procedures are compared as well. CONCLUSION: This approach is easy to implement and fast in computation for both binary and multiclass problems. The gene set provided by the RLS-SVR weight-based approach contains a less number of genes, and achieves a higher accuracy than other procedures.

Genetic and environmental influences on schizotypy among adolescents in Taiwan: a multivariate twin/sibling analysis.

This study aimed to examine the relative contribution of genes and environment to psychometrically measured schizotypy and the causes for the covariation between different dimensions of schizotypy in a total of 330 pairs of twins and 36 same-sex sib-pairs aged 12-16 and systematically recruited from junior high schools in Taipei. Twins' zygosity was determined by a combination of DNA typing and physical similarity. Schizotypy was measured using the Perceptual Aberration Scale (PAS) as well as the Schizotypal Personality Questionnaire (SPQ) and its three factors (Cognitive-perceptual Dysfunction, Disorganization, and Interpersonal Dysfunction). Univariate analyses of structural equation modeling using Mx program showed that scores on these schizotypal measures were substantially heritable (h (2) ranging from 41 to 49%), with some genetic effects being non-additive. Multivariate analyses revealed common genetic factors linking between various traits of schizotypy, with bivariate heritability ranging from 50 to 65%. The proportion of the genetic contributions not shared with the other measures of schizotypy ranged from 24% for the Disorganization to 49% for the PAS scores. We concluded that there exist both common and specific genetic factors between the various dimensions of schizotypy, and at least half of their correlations were genetic in nature.

Hepatitis B e antigen and the risk of hepatocellular carcinoma.

BACKGROUND: The presence of hepatitis B e antigen (HBeAg) in serum indicates active viral replication in hepatocytes. HBeAg is thus a surrogate marker for the presence of hepatitis B virus DNA. We conducted a prospective study to determine the relation between positivity for hepatitis B surface antigen (HBsAg) and HBeAg and the development of hepatocellular carcinoma. METHODS: In 1991 and 1992, we enrolled 11,893 men without evidence of hepatocellular carcinoma (age range, 30 to 65 years) from seven townships in Taiwan. Serum samples obtained at the time of enrollment were tested for HBsAg and HBeAg by radioimmunoassay. The diagnosis of hepatocellular carcinoma was ascertained through data linkage with the computerized National Cancer Registry in Taiwan and with death certificates. We performed a multiple regression analysis to determine the relative risk of hepatocellular carcinoma among men who were positive for HBsAg alone or for HBsAg and HBeAg, as compared with those who were negative for both. RESULTS: There were 111 cases of newly diagnosed hepatocellular carcinoma during 92,359 person-years of follow-up. The incidence rate of hepatocellular carcinoma was 1169 cases per 100,000 person-years among men who were positive for both HBsAg and HBeAg, 324 per 100,000 person-years for those who were positive for HBsAg only, and 39 per 100,000 person-years for those who were negative for both. After adjustment for age, sex, the presence or absence of antibodies against hepatitis C virus, cigarette-smoking status, and use or nonuse of alcohol, the relative risk of hepatocellular carcinoma was 9.6 (95 percent confidence interval, 6.0 to 15.2) among men who were positive for HBsAg alone and 60.2 (95 percent confidence interval, 35.5 to 102.1) among those who were positive for both HBsAg and HBeAg, as compared with men who were negative for both. CONCLUSIONS: Positivity for HBeAg is associated with an increased risk of hepatocellular carcinoma.

The efficacy of brimonidine in preventing intraocular pressure elevation in the provocative test for primary angle-closure glaucoma.

The purpose of this study was to evaluate the efficacy of 0.2% brimonidine tartrate in preventing intraocular pressure (IOP) elevation in the dark-prone provocative test for primary angle-closure glaucoma (PACG). Twenty-two eyes from 22 patients with angle-closure glaucoma were enrolled in this study. Each of the selected eyes had previously tested positive in a recent dark-prone test. One drop of 0.2% brimonidine tartrate was then instilled in each eye 2 hours prior to a second dark-prone test. An IOP elevation of greater than 8 mmHg was regarded as a positive result. The IOP elevation in the first dark-prone test was 11.91 +/- 5.17 (range: 5.7 - 27.3) mmHg, while the IOP only increased 5.70 - 2.96 (range: 2.9 - 12.2) mmHg in the second dark-prone test that was pre-treated with 0.2% brimonidine tartrate (p < 0.001). A significant difference was also noted in the pre-test IOP (15.59 +/- 3.86 mmHg vs. 13.33 +/- 3.65 mmHg, p = 0.008) as well as in the post-test IOP (27.62 +/- 7.27 mmHg vs. 19.03 +/- 3.50 mmHg, p < 0.001) in the two sequential dark-prone tests. All but three of the initially positive dark-prone tests (86.46%) converted to negative tests after pre-treatment with brimonidine. There was a significant effect of 0.2% brimonidine tartrate in the prevention of IOP elevation in PACG patients previously found to test positive in the dark-prone provocative test.