Ketoconazole associated hepatotoxicity: a systematic review and meta- analysis.

OBJECTIVE: To evaluate the incidence of Ketoconazole associated hepatotoxicity and related factor. METHODS: Literature retrieval was conducted by using multi-databases for meta-analysis on Ketoconazole associated hepatotoxicity. The data were collected with a standardized form. Overall estimation of incidence of hepatotoxicity for specific study type was calculated by using a DerSimonian-Laird random-effects model owing to the substantial differences among the studies. RESULTS: Totally 204 eligible studies were included in the analysis. The incidence of Ketoconazole associated hepatotoxicity was 3.6%-4.2%. The dosage and duration specific subgroup analyses did not show any significant difference among groups, while the age specific subgroup analysis showed the incidence in children and people aged >60 years was 1.4% (95% CI: 0.5%-4.2%) and 3.2% (95% CI: 1.1%-8.7%) respectively. Additionally, the incidence of the hepatotoxicity was higher in people who had oral administration of ketoconazole beyond the provisions of the usage instructions, and the incidence was 5.7% (95% CI: 4.5%-7.2%). CONCLUSION: Ketoconazole associated hepatotoxicity was common. Off-label use might increase the risk of liver damage. Well-designed large sample studies are needed to identify the risk factors in future.

[Association analysis of serum γ-glutamyltransferase with risk of metabolic syndrome in Beijing healthy population].

OBJECTIVE: To study the association of γ-glutamyltransferase (GGT) with the development of the metabolic syndrome (MS). METHODS: Subjects without MS at baseline in Beijing health-checkup database during 2003 and 2010, from MJ Health Management Centers, with complete key variables and at least two records were selected to derive a cohort, after comparison of the median trend, and analysis with Cox regression models and spline regression models, and to study the association of GGT with the development of MS and the dose-response relationship trend. RESULTS: Out of 10 076 (46.20/1 000 person-years) in the cohort, 1 181 subjects developed MS after follow-up of 2.54 years on average. With adjustment for age, gender, cigarette smoking, alcohol intake, physical activity, body mass index, family history of cardiovascular disease, systolic blood pressure, white blood cell count, high-density lipoprotein cholesterol, fasting blood glucose, triglycerides and C-reacted protein in Cox regression model, the hazard ratio for MS in quartiles 4 level of GGT was 1.60(95% confidence interval: 1.18-2.17). After adjustment with the use of spline regression model, the dose-response relationship showed an increasing curve with a degressive slope. The elevated GGT level was associated with an increased risk of MS, but the contribution of GGT augmented less when the GGT level was high. CONCLUSION: The elevated GGT level, an important risk factor and predictor, may be associated with an increased risk of MS.

[Estimation on the risk of 5-years obesity development among adults aged 30 - 59, based on the Taiwan MJ Health-checkup Database].

OBJECTIVE: This study aimed to provide an epidemiological modeling in evaluating the risk of developing obesity within 5 years in Taiwan population aged 30 - 59 years. METHODS: After excluding 918 individuals who were observed at baseline, a cohort of 14 167 non-obesity subjects aged 30 - 59 years in the initial year during 1998 - 2006, was formed to derive a Risk Score which could predict the incident obesity (IO). Multivariate logistic regression was used to derive the risk functions, using the check-up center (Taipei training cohort, n = 8104) of the overall cohort. Rules based on these risk functions were evaluated in the left three centers (testing cohort, n = 6063). Risk functions were produced to detect the IO on a training sample using the multivariate logistic regression models. Starting with variables that could predict the IO through univariate models, we constructed multivariable logistic regression models in a stepwise manner which eventually could include all the variables. We evaluated the predictability of the model by the area under the receiver-operating characteristic (ROC) curve (AUC) and to testify its diagnostic property on the testing sample. Once the final model was defined, the next step was to establish rules to characterize 4 different degrees of risk based on the cut points of these probabilities after transforming into normal distribution by log-transformation. RESULTS: At baseline, the range of the proportion of normal weight, overweight and obesity were 50.00% - 60.00%, 26.47% - 31.11% and 5.76% - 7.24% respectively in four check-up centers of Taiwan. After excluding 918 obesity individuals at baseline, we ascertained 386 (2.73%, 386/14 167) cases having IO and 2.66% - 2.91% of them having centered obesity in the four check-up centers respectively. Final multivariable logistic regression model would include five risk factors: sex, age, history of diabetes, weight deduction ≥ 4 kg within 3 months and waist circumference. The area under the ROC curve (AUC) was 0.898 (95%CI, 0.884 - 0.912) that could predict the development of obesity within 5 years. The curve also had adequate performance in testing the sample [AUC = 0.881 (95%CI, 0.862 - 0.900)]. After labeling the four risk degrees, 16.0% and 2.9% of the total subjects were in the mediate and high risk populations respectively and were 7.8 and 16.6 times higher, when comparing with the population at risk in general. CONCLUSION: The predictability and reliability of our obesity risk score model, derived based on Taiwan MJ Longitudinal Health-checkup-based Population Database, were relatively satisfactory, with its simple and practicable predictive variables and the risk degree form. This model could help individuals to self assess the situation of risk on obesity and could also guide the community caretakers to monitor the trend of obesity development.