No abstract

We examined whether fatty liver, as diagnosed with abdominal ultrasonography , is an independent risk factor for diabetes mellitus during 10 years of follow-up . A total of 840 subjects (467 men and 373 women) were followed for the entire 10 years. The criteria for being non-diabetic were having no history of diabetes, having a fasting plasma glucose level of less than 110 mg/dl and a serum hemoglobin Air level of 6.4% or less. We indicated that every examine received all examinations after 12 hours of fasting. Well-trained technicians performed abdominal ultrasonography . Although univariate analysis revealed that the presence of fatty liver was related to hyperglycemia 10 years later, multiple logistic regression analysis did not support this finding. In the multiple logistic regression analysis fasting plasma glucose levels at the baseline and age were significantly related to hyperglycemia (odds ratio [OR] = 1.16, 95% confidence interval [Cl]: 1 .11-1.21, OR = 1.07, 95% Cl: 1.01-1.14, respectively). Fatty liver was not an independent risk factor for hyperglycemia in our follow-up study 10 years after the first diagnosis. The high fasting plasma glucose levels were a risk factor for diabetes, even in the normal range. J Epidemio/2003;13:15-21.

ObjectiveThe contributions of highly correlated cardiovascular risk factors to intraocular pressure (IOP) are not clear due to underlying confounding problems. The present study aimed to determine which metabolic syndrome parameters contribute to elevating IOP and to what extent.DesignRetrospective cohort study.SettingA private healthcare centre in Japan.ParticipantsIndividuals who visited a private healthcare centre and underwent comprehensive medical check-ups between April 1999 and March 2009 were included (20 007 in the cross-sectional study and 15 747 in the longitudinal study).Primary and secondary outcome measuresChanges in IOP were evaluated in terms of ageing and changes in metabolic syndrome parameters. Pearson's correlation coefficients and mixed-effects models were used to examine the relationship of changes in IOP with ageing and changes in metabolic syndrome parameters in cross-sectional and longitudinal studies, respectively.ResultsIn the cross-sectional study, IOP was negatively correlated with age and positively correlated with waist circumference, high-density lipoprotein cholesterol (HDL-C) levels, triglyceride levels, systolic blood pressure (SBP), diastolic blood pressure (DBP) and fasting plasma glucose (FPG) levels. In the longitudinal multivariate analysis, the associated IOP changes were −0.12 (p<0.0001) mm Hg with male sex; −0.59 (p<0.0001) mm Hg with 10 years of ageing; +0.42 (p<0.0001) mm Hg with 1 mmol/L increase in HDL-C levels; +0.092 (p<0.0001) mm Hg with 1 mmol/L increase in triglyceride levels; +0.090 (p<0.0001) mm Hg with 10 mm Hg increase in SBP; +0.085 (p<0.0001) mm Hg with 10 mm Hg increase in DBP; and+0.091 (p<0.0001) mm Hg with 1 mmol/L increase in FPG levels.ConclusionsElevation of IOP was related to longitudinal worsening of serum triglyceride levels, blood pressure and FPG and improvement in serum HDL-C levels.

Background As a first‐line therapy for Helicobacter pylori, dual therapy with vonoprazan and amoxicillin (VA‐dual) provides an eradication rate similar to that of vonoprazan‐based triple therapy. As the factors associated with the eradication rate of H. pylori with VA‐dual are unknown,we investigated them in this study. Materials and Methods Overall, 163 patients diagnosed with H. pylori infection received VA‐dual (vonoprazan 20 mg twice daily and amoxicillin 750 mg twice daily for 7 d). The association between successful H. pylori eradication and the following patient clinical factors was analyzed: sex, age, height, weight, body surface area (BSA), body mass index (BMI), history of early gastric carcinoma and peptic ulcer, comorbidity of cirrhosis, alcohol consumption habit, smoking habit, common use of proton pump inhibitors, and concomitant use of drugs that are substratesof cytochrome P450 (CYP) 3A4. The association between post‐eradication adverse events and clinical factors was analyzed retrospectively. Results Successful H. pylori eradication was associated with a lower BSA (eradication rate: 90.8% in patients with BSA <1.723 vs. 79.6% in those with BSA ≥1.723; p = 0.045). The incidence of adverse events was higher in women than in men (adverse events: 40.0% in women vs. 19.4% in men; p = 0.004). Conclusions Successful H. pylori eradication with VA‐dual was associated with the small body size of patients. This therapy may have to be adjusted per body size.

IntroductionEarly intervention in type 2 diabetes can prevent exacerbation of insulin resistance. More effective interventions can be implemented by early and precise prediction of the change in glycated haemoglobin A1c (HbA1c). Artificial intelligence (AI), which has been introduced into various medical fields, may be useful in predicting changes in HbA1c. However, the inability to explain the predictive factors has been a problem in the use of deep learning, the leading AI technology. Therefore, we applied a highly interpretable AI method, random forest (RF), to large-scale health check-up data and examined whether there was an advantage over a conventional prediction model.Research design and methodsThis study included a cumulative total of 42 908 subjects not receiving treatment for diabetes with an HbA1c <6.5%. The objective variable was the change in HbA1c in the next year. Each prediction model was created with 51 health-check items and part of their change values from the previous year. We used two analytical methods to compare the predictive powers: RF as a new model and multivariate logistic regression (MLR) as a conventional model. We also created models excluding the change values to determine whether it positively affected the predictions. In addition, variable importance was calculated in the RF analysis, and standard regression coefficients were calculated in the MLR analysis to identify the predictors.ResultsThe RF model showed a higher predictive power for the change in HbA1c than MLR in all models. The RF model including change values showed the highest predictive power. In the RF prediction model, HbA1c, fasting blood glucose, body weight, alkaline phosphatase and platelet count were factors with high predictive power.ConclusionsCorrect use of the RF method may enable highly accurate risk prediction for the change in HbA1c and may allow the identification of new diabetes risk predictors.