PURPOSE: Identifying microbial communities with 16S ribosomal RNA (rRNA) gene sequencing is a popular approach in microbiome studies, and various software tools and data resources have been developed for microbial analysis. Our aim in this study is investigating various available software tools and reference sequence databases to compare their performance in differentiating subject samples and negative controls. METHODS: We collected 4 negative control samples using various acquisition protocols, and 2 respiratory samples were acquired from a healthy subject also with different acquisition protocols. Quantitative methods were used to compare the results of taxonomy compositions of these 6 samples by varying the configuration of analysis software tools and reference databases. RESULTS: The results of taxonomy assignments showed relatively little difference, regardless of pipeline configurations and reference databases. Nevertheless, the effect on the discrepancy was larger using different software configurations than using different reference databases. In recognizing different samples, the 4 negative controls were clearly separable from the 2 subject samples. Additionally, there is a tendency to differentiate samples from different acquisition protocols. CONCLUSION: Our results suggest little difference in microbial compositions between different software tools and reference databases, but certain configurations can improve the separability of samples. Changing software tools shows a greater impact on results than changing reference databases; thus, it is necessary to utilize appropriate configurations based on the objectives of studies.
Classification ; Computational Biology ; Healthy Volunteers ; Metagenome ; Microbiota ; RNA, Ribosomal, 16S

Background: The number of patients with dementia continues to increase as the age of aging continues to grow. Psychiatric symptoms caused by senile dementia are controlled using antipsychotics. However, these antipsychotics can lead to Parkinson's disease, and abuse of dopamine derivatives such as levodopa among Parkinsonian drugs can lead to psychosis. Therefore, we evaluated the patterns of prescribed antipsychotics and antiparkinsonian drugs in patients with senile dementia. Methods: We used data from the sample of elderly patients from the Health Insurance Review and Assessment Service (HIRA-APS-2016). We analyzed the patterns of prescribing antipsychotics and antiparkinsonian drugs including prescribed daily dosage, period of prescription, and number of patients with both antipsychotics and antiparkinsonian drugs for senile dementia. Results: Among the 159,391 patients with dementia included in this analysis, 4,963 patients (3.1%) and 16,499 patients (10.4%) were prescribed typical and atypical antipsychotic drugs, respectively. The most frequently prescribed typical antipsychotic was haloperidol (4,351 patients with dementia), whereas the atypical agent was quetiapine (12,719 patients). The most frequently prescribed antiparkinsonian drugs were in the order of levodopa/carbidopa, benztropine, and ropinirole. In addition, 1,103 and 3,508 patients prescribed typical and atypical antipsychotics, respectively, were co-prescribed antiparkinsonian drugs. Conclusions Atypical antipsychotics were the preferred prescription in patients with senile dementia. The prescription dose was relatively low; however, the average treatment duration was mostly long-term. Selection of antipsychotics and/or antiparkinsonian drugs should be made carefully in senile dementia and the causal relationship of adverse drug reactions needs further study.

Background: Age-adjusted bone mineral density (BMD) in postmenopausal women decreases in developed countries whereas incidence of osteoporotic fracture decreases or remains stable. We investigated secular trends of bone density from 2008 to 2017 among different age groups of postmenopausal women. Methods: We analyzed BMD data obtained from health check-ups of 4,905 postmenopausal women during three survey cycles from 2008 to 2017. We divided them into 3 groups by age (50–59 years, 60–69 years, and 70 years or more) and observed the transition of lumbar and femoral BMD in each group, before and after adjusting for variables that may affect BMD. Results: Age-adjusted BMD, bone mineral content (BMC), and T-score demonstrated a declining trend over the survey period at lumbar spine (−2.8%), femur neck (−3.5%) and total femur (−4.3%), respectively. In the analysis for the age groups, the BMD, BMC, and T-score presented linear declining trend (−6.1%) in younger postmenopausal women while women aged over 70 or more showed linear increasing trends (+6.3%) at lumbar spine during the survey period. Femoral neck and total femur BMD demonstrated a declining linear trend only in the 50–59 and 60–69 years groups (−5.5%, −5.2%, respectively), but not in the 70 years or more group. Conclusion BMD in younger postmenopausal women has decreased considerably but has increased or plateaued in elderly women. This discordance of BMD trends among different age groups may contribute to decreased incidence of osteoporotic fracture despite a recent declining BMD trend in postmenopausal women.

BACKGROUND/OBJECTIVES: Korea is quickly becoming an aged society. Dementia is also becoming a vital public health problem in Korea. Cognitive impairment as a pre-stage of dementia shares most risk factors for dementia. The aim of the present study was to determine associations of serum levels of vitamins A, C, and E with the risk of cognitive impairment among elderly Koreans. SUBJECTS/METHODS: In this cross-sectional study, a total of 230 participants aged 60–79 years from Yangpyeong cohort were included. Cognitive function was assessed by the Korean version of the Mini-Mental State Examination for Dementia Screening. The logistic multivariable regression model was applied to determine the effect of serum vitamins A, C, and E on the risk of cognitive impairment. RESULTS: There was no significant association between the risk of cognitive impairment and serum levels of vitamin A and vitamin C. There was a significant odd ratio when the second tertile group of beta-gamma tocopherol level was compared to the first tertile group [odds ratio (OR) = 0.37, 95% confidence interval (CI) = 0.14–0.98, P for trend = 0.051]. In subgroup analyses, there were significant negative associations between beta-gamma tocopherol level and the risk of cognitive impairment in men (OR = 0.17, 95% CI = 0.03–0.87, P for trend = 0.028), non-drinkers or former drinkers (OR = 0.13, 95% CI = 0.02–0.66, P for trend = 0.025), and non-smokers or former smokers (OR = 0.27, 95% CI = 0.09–0.82, P for trend = 0.017). CONCLUSION: Serum beta-gamma tocopherol levels tended to be inversely associated with the risk of cognitive impairment. Further prospective large-scaled studies are needed to examine this association.
Aged* ; Ascorbic Acid ; Cognition ; Cognition Disorders* ; Cohort Studies ; Cross-Sectional Studies ; Dementia ; Humans ; Korea ; Male ; Mass Screening ; Prospective Studies ; Public Health ; Risk Factors ; Tocopherols ; Vitamin A ; Vitamin E ; Vitamins*

Purpose: Ulcerative colitis (UC) is known to have an association with the increased risk of colorectal cancer (CRC), and UC-associated CRC does not follow the typical progress pattern of adenoma-carcinoma. The aim of this study is to investigate molecular characteristics of UC-associated CRC and further our understanding of the association between UC and CRC. Methods: From 5 patients with UC-associated CRC, matched normal, dysplasia, and tumor specimens were obtained from formalin-fixed paraffin-embedded (FFPE) samples for analysis. Genomic DNA was extracted and whole exome sequencing was conducted to identify somatic variations in dysplasia and tumor samples. Statistical analysis was performed to identify somatic variations with significantly higher frequencies in dysplasia-initiated tumors, and their relevant functions were investigated. Results: Total of 104 tumor mutation genes were identified with higher mutation frequencies in dysplasia-initiated tumors. Four of the 5 dysplasia-initiated tumors (80.0%) have TP53 mutations with frequent stop-gain mutations that were originated from matched dysplasia. APC and KRAS are known to be frequently mutated in general CRC, while none of the 5 patients have APC or KRAS mutation in their dysplasia and tumor samples. Glycoproteins including mucins were also frequently mutated in dysplasia-initiated tumors. Conclusion UC-associated CRC tumors have distinct mutational characteristics compared to typical adenoma-carcinoma tumors and may have different cancer-driving molecular mechanisms that are initiated from earlier dysplasia status.

PURPOSE: This study was designed to validate the usefulness of the Acute Physiology and Chronic Health Evaluation (APACHE) II for predicting hospital mortality of critically ill Korean patients. MATERIALS AND METHODS: We analyzed data on 826 patients who had been admitted to nine intensive care units and were included in the Fever and Antipyretics in Critical Illness Evaluation study cohort. RESULTS: Among the patients enrolled, 62% (512/826) were medical and 38% (314/826) were surgical patients. The median APACHE II score was 17 (11 to 23 interquartile range), and the hospital mortality rate was 19.5%. Age, underlying diseases, medical patients, mechanical ventilation, and renal replacement therapy were independently associated with hospital mortality. The calibration of APACHE II was poor (H=57.54, p<0.0001; C=55.99, p<0.0001), and the discrimination was modest [area under the receiver operating characteristic (aROC)=0.729]. Calibration was poor for both medical and surgical patients (H=63.56, p<0.0001; C=73.83, p<0.0001, and H=33.92, p<0.0001; C=33.34, p=0.0001, respectively), while discrimination was poor for medical patients (aROC=0.651) and modest for surgical patients (aROC=0.704). At the predicted risk of 50%, APACHE II had a sensitivity of 36.6% and a specificity of 87.4% for hospital mortality. CONCLUSION: For Koreans, the APACHE II exhibits poor calibration and modest discrimination for hospital mortality. Therefore, a new model is needed to accurately predict mortality in critically ill Korean patients.
*APACHE ; Aged ; Cohort Studies ; Critical Illness/mortality ; Hospital Mortality ; Humans ; *Intensive Care Units ; Middle Aged ; Risk Factors

Background: Hong Kong's construction industry currently faces a manpower crisis. Blue-collar workers are a disadvantaged group and suffer higher levels of chronic diseases, for example, cancer, than the wider population. Cancer risk factors are likely to cluster together. We documented prevalence of cancer-associated lifestyle risk behaviors and their correlates among Hong Kong construction workers. Methods: Data were collected from workers at 37 railway-related construction worksites throughout Hong Kong during May 2014. Tobacco use, alcohol consumption, unbalanced nutrition intake, and physical inactivity were included in the analysis. Latent class analysis and multivariable logistic regression were performed to identify the patterns of risk behaviors related to cancer, as well as their impact factors among construction workers in Hong Kong. Results: Overall, 1,443 workers participated. Latent class analysis identified four different behavioral classes in the sample. Fully adjusted multiple logistic regression identified age, gender, years of Hong Kong residency, ethnicity, educational level, and living status differentiated behavioral classes. Conclusion High levels of lifestyle-related cancer-risk behaviors were found in most of the Hong Kong construction workers studied. The present study contributes to understanding how cancer-related lifestyle risk behaviors cluster among construction workers and relative impact factors of risk behaviors. It is essential to tailor health behavior interventions focused on multiple risk behaviors among different groups for further enlarging the effects on cancer prevention.

Purpose: Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. Methods: We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. Results: Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. Conclusions The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.

Purpose: Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. Methods: We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. Results: Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. Conclusions The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.