A reduced risk of stroke with lithium exposure in bipolar disorder: a population‐based retrospective cohort study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115969/1/bdi12336.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/115969/2/bdi12336_am.pd

Socio-demographic and health-related factors associated with cognitive impairment in the elderly in Taiwan

<p>Abstract</p> <p>Background</p> <p>Cognitive impairment is an age-related condition as the rate of cognitive decline rapidly increases with aging. It is especially important to better understand factors involving in cognitive decline for the countries where the older population is growing rapidly. The aim of this study was to examine the association between socio-demographic and health-related factors and cognitive impairment in the elderly in Taiwan.</p> <p>Methods</p> <p>We analysed data from 2119 persons aged 65 years and over who participated in the 2005 National Health Interview Survey. Cognitive impairment was defined as having the score of the Mini Mental State Examination lower than 24. The χ²test and multiple logistic regression models were used to evaluate the association between cognitive impairment and variables of socio-demography, chronic diseases, geriatric conditions, lifestyle, and dietary factors.</p> <p>Results</p> <p>The prevalence of cognitive impairment was 22.2%. Results of multivariate analysis indicated that low education, being single, low social support, lower lipid level, history of stroke, physical inactivity, non-coffee drinking and poor physical function were associated with a higher risk of cognitive impairment.</p> <p>Conclusion</p> <p>Most of the characteristics in relation to cognitive impairment identified in our analysis are potentially modifiable. These results suggest that improving lifestyle behaviours such as regular exercise and increased social participation could help prevent or decrease the risk of cognitive impairment. Further investigations using longitudinal data are needed to clarify our findings.</p

Bone mass in schizophrenia and normal populations across different decades of life

<p>Abstract</p> <p>Background</p> <p>Chronic schizophrenic patients have been reported as having higher osteoporosis prevalence. Survey the bone mass among schizophrenic patients and compare with that of the local community population and reported data of the same country to figure out the distribution of bone mass among schizophrenic patients.</p> <p>Methods</p> <p>965 schizophrenic patients aged 20 years and over in Yuli Veterans Hospital and 405 members aged 20 and over of the community living in the same town as the institute received bone mass examination by a heel qualitative ultrasound (QUS) device. Bone mass distribution was stratified to analyzed and compared with community population.</p> <p>Results</p> <p>Schizophrenic patients have lower bone mass while they are young. But aging effect on bone mass cannot be seen. Accelerated bone mass loss during menopausal transition was not observed in the female schizophrenic patients as in the subjects of the community female population.</p> <p>Conclusion</p> <p>Schizophrenic patients have lower bone mass than community population since they are young. Further study to investigate the pathophysiological process is necessary to delay or avoid the lower bone mass in schizophrenia patients.</p

A Gee-Based Regression Model on Genetic Variability of Endophenotypes in Schizophrenic Families

Introduction: Schizophrenia has been widely identified as a multifactorial disorder, and prevalent endophenotypes including cognitive function, attention, or symptom clusters , among families schizophrenic proband were targeted for etiological studies. Methods: We recruited from two sample- collecting programs the multidimensional psychopathology study of schizophrenia (MPSS, 86 families) from 1993 to 2001 and the Taiwan schizophrenia linkage study (TSLS, 132 families) from 1998 to 2002. Subjects were interviewed by the research psychiatrists using the Psychiatrist Diagnostic Assessment (PDA) or the Mandarin Chinese version of the Diagnostic Interview for Genetic Studies (DIGS). The final diagnostic assessment was formulated by integrating either the PDA or the DIGS data and clinical information of medical chart records according to DSM-IV. A total of 218 schizophrenic nuclear families with at least two affected siblings of 1016 subjects were participated in this study. The genomic DNA samples were SNP genotyped on 9 candidate genes. Participants were also measured by using WCST, CPT, and PANSS. Results: The explanatory ability of all genotypes within 9 candidate regions after adjusted for sex and age based on GEE distribution varies from 0.27 (disorganized symptom cluster in PANSS) to 0.72 (Z score in WCST). Conclusions: It suggested that the target endophenotype with a higher genetic composition might be differentiated from others by using the GEE-based regression model

An Emerging Complimentary Medicine-Yolk Oil Made from Heating Method

Yolk oil is common in Asia. According to the Flora Sinensis, yolk oil is a multipurpose medicine, with specific dermatological and fever indications. Nowadays, it is generally used as a complimentary medicine for heart diseases. Yolk oil can be made from heating or chemical extraction method. It is generally believed that yolk oil made from heating (YOheat) method is more effective as a medicine than that from extraction (YOext). The technical details of the heating method remain an issue of argument, including the degree of char and the threat of carcinogens formed during the heating process. Most yolk oil related studies used YOext as research material. Nevertheless, animal studies have showed that YOheat reduced triglycerides and total cholesterol in rodent liver. It is expected an easy-to-make complimentary medicine like YOheat may become even more common and thus evidence based studies should be conducted to verify its pharmacological effects and safety

Physical Activity Might Reduce the Adverse Impacts of the FTO Gene Variant rs3751812 on the Body Mass Index of Adults in Taiwan

The fat mass and obesity-associated (FTO) gene is a significant genetic contributor to polygenic obesity. We investigated whether physical activity (PA) modulates the effect of FTO rs3751812 on body mass index (BMI) among Taiwanese adults. Analytic samples included 10,853 Taiwan biobank participants. Association of the single-nucleotide polymorphism (SNP) with BMI was assessed using linear regression models. Physical activity was defined as any kind of exercise lasting 30 min each session, at least three times a week. Participants with heterozygous (TG) and homozygous (TT) genotypes had higher BMI compared to those with wild-type (GG) genotypes. The &#946; value was 0.381(p &lt; 0.0001) for TG individuals and 0.684 (p = 0.0204) for TT individuals. There was a significant dose-response effect among carriers of different risk alleles (p trend &lt;0.0001). Active individuals had lower BMI than their inactive counterparts (&#946; = &#8722;0.389, p &lt; 0.0001). Among the active individuals, significant associations were found only with the TG genotype (&#946; = 0.360, p = 0.0032). Inactive individuals with TG and TT genotypes had increased levels of BMI compared to those with GG genotypes: Their &#946; values were 0.381 (p = 0.0021) and 0.950 (p = 0.0188), respectively. There was an interaction between the three genotypes, physical inactivity, and BMI (p trend&#8201; = 0.0002). Our data indicated that increased BMI owing to genetic susceptibility by FTO rs3751812 may be reduced by physical activity

The Autonomic Progress Bar Motivates Treatment Completion for Patients of Stimulant Use Disorder and Cannabis Use Disorder

Background: The intrinsic motivation behind the "need to complete" is more influential than external incentives. We introduced a novel progress-bar tool to motivate the completion of programs designed to treat stimulant and cannabis use disorders. We further examined the effectiveness of the progress bar's scoring approach in forecasting consistently negative urine tests. Methods: This study's participants included 568 patients with stimulant, amphetamine-type, and cannabis use disorders who were undergoing 12-month mandatory treatment programs at Taichung Veterans General Hospital in Taiwan. Patients were given scores of 1, -1, or 0 depending on whether they received negative, positive, or missing urinalysis reports, respectively. The autonomic progress bar generated weekly score totals. At the group level, scorei donated scores from all patients for a given week (i denoted the week). Scorei was standardized to adjusted scorei. We then conducted Autoregressive Integrated Moving Average (ARIMA) Model of time-series analyses for the adjusted scorei. Results: A total of 312 patients maintained treatment progress over the 12-month program. The autonomic score calculator totaled the shared achievements of these patients. The coefficients of the lag variables for mean (p), lag variables for residual error term (q), and number of orders for ensuring stationary (d) were estimated at p = 3, d = 4, and q = 7 for the first half of the treatment program, and were estimated at p = 2, d = 2, and q = 3 for the second half. Both models were stationary and tested as fit for prediction (p < 0.05). Sharply raised adjusted scores were predicted during the high-demand treatment phase. Discussion: This study's novel progress-bar tool effectively motivated treatment completion. It was also effective in forecasting continually negative urine tests. The tool's free open-source code makes it easy to implement among many substance-treatment services

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.

BackgroundIntelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data for estimating stroke risk needs to be assessed and validated. This study aims to apply a DNN for deriving a stroke predictive model using a big electronic health record database.Methods and resultsThe Taiwan National Health Insurance Research Database was used to conduct a retrospective population-based study. The database was divided into one development dataset for model training (~70% of total patients for training and ~10% for parameter tuning) and two testing datasets (each ~10%). A total of 11,192,916 claim records from 840,487 patients were used. The primary outcome was defined as any ischemic stroke in inpatient records within 3 years after study enrollment. The DNN was evaluated using the area under the receiver operating characteristic curve (AUC or c-statistic). The development dataset included 672,214 patients (a total of 8,952,000 records) of whom 2,060 patients had stroke events. The mean age of the population was 35.5±20.2 years, with 48.5% men. The model achieved AUC values of 0.920 (95% confidence interval [CI], 0.908-0.932) in testing dataset 1 and 0.925 (95% CI, 0.914-0.937) in testing dataset 2. Under a high sensitivity operating point, the sensitivity and specificity were 92.5% and 79.8% for testing dataset 1; 91.8% and 79.9% for testing dataset 2. Under a high specificity operating point, the sensitivity and specificity were 80.3% and 87.5% for testing dataset 1; 83.7% and 87.5% for testing dataset 2. The DNN model maintained high predictability 5 years after being developed. The model achieved similar performance to other clinical risk assessment scores.ConclusionsUsing a DNN algorithm on this large electronic health record database is capable of obtaining a high performing model for assessment of ischemic stroke risk. Further research is needed to determine whether such a DNN-based IDSS could lead to an improvement in clinical practice

Incidence and Risk Factors of Workplace Violence on Nursing Staffs Caring for Chronic Psychiatric Patients in Taiwan

This one-year follow-up study determined the incidence and risk factors of workplace violence against nursing staff in a psychiatric hospital. The cohort members had a website to report events whenever they came across violence. A total of 971 events were reported. The incidence rates of physical violence, verbal abuse, bullying/mobbing, sexual harassment, and racial harassment were 1.7, 3.7, 0.2, 0.3, and 0 per staff-year, respectively. Young age, female sex, lower education, shorter duration of employment, and high level of anxiety of staff seemed to be the determinants of violence. Pre-placement education should focus on these staff to reduce workplace violence