Association between serumα-L-fucosidase andnon-alcoholic fatty liver disease:Cross-sectional study

AIM: To explore the association between serum α-Lfucosidase(Af U) and non-alcoholic fatty liver disease(NAf LD).METHODS: A total of 16473 individuals(9456 men and 7017 women) were included in the current study, who presented for a health examination at the first Affiliated hospital of Zhejiang University School of medicine in 2014. The baseline characteristics of the cohort were compared by NAf LD status. Linear regression analysis and stepwise multiple regression analysis were applied to assess the risk factors for NAf LD. Receiver operating characteristic curve was used to determine the sensitivity and specificity of Af U in the diagnosis of NAf LD.RESULTS: The prevalence rates of NAf LD and metabolic syndrome(met S) were 38.0% and 25.4%, respectively. The NAf LD group had significantly higher Af U levels than the non-NAf LD group(28.7 ± 7.9 U/L vs 26.0 ± 7.3 U/L, P < 0.001) and the prevalence rate of NAf LD increased with progressively higher serum Af U levels. Af U was positively correlated with met S and its five components: central obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol, and elevated blood pressure and fasting glucose. Stepwise multiple logistic regression analysis showed that Af U was associated with an increased risk of NAf LD(OR = 1.009, 95%CI: 1.003-1.014, P < 0.001). The best cut-off value of Af U for the diagnosis of NAf LD was 27.5 U/L. The area under the curve(diagnostic efficacy index) was 0.606. The sensitivity and specificity were 54.6% and 61.8%, respectively. CONCLUSION: Af U level is significantly associated with NAf LD, and elevated Af U level is an independent risk factor for NAf LD.

Association between ADIPOQ gene polymorphisms and the risk of new-onset diabetes mellitus after liver transplantation

BACKGROUND:New-onset diabetes after transplantation(NODAT) has become one of the major factors that affect the overall survival and long-term life quality in liver transplantation(LT) recipients. Previous studies found that the serum adiponectin concentration of diabetic patients is significantly lower than that of healthy subjects. Adiponectin regulates the blood glucose level by increasing body sensitivity to insulin through various mechanisms. In this study, we aimed to investigate the impact of diabetes related gene polymorphisms on the development of NODAT in liver recipients.METHODS:A total of 256 LT patients in a single-center were selected retrospectively for the study. Genomic DNA was extracted from explanted liver tissues, and tested for twelve diabetes mellitus associated single nucleotide polymorphisms by Sequenom Mass ARRAY. Modified clinical models in predicting NODAT were established and evaluated.RESULTS:The GG genotype of ADIPOQ rs1501299 gene polymorphism was significantly more frequent in NODAT than non-NODAT LT patients(56% vs 39%, P=0.014). Dominant model(GG vs GT+TT, P=0.030) and recessive model(GT+GG vs TT, P=0.005) also confirmed the genotype distribution difference between NODAT and non-NODAT groups. Age(OR=1.048, P=0.004), BMI(OR=1.107, P=0.041), and blood tacrolimus level at 1-month LT(OR=1.170, P=0.003) were clinical independent risk factors of NODAT. Furthermore, rs1501299 could improve the ability of clinical model in predicting NODAT(AUROC=0.743, P<0.001).CONCLUSION:ADIPOQ rs1501299 gene polymorphism is associated with an increased risk of NODAT, which should be added to the clinical models in predicting the occurrence of NODAT in LT recipients.

Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease:An extended study

Background:Nonalcoholic fatty liver disease(NAFLD)is a public health challenge and significant cause of morbidity and mortality worldwide.Early identification is crucial for disease intervention.We recently proposed a nomogram-based NAFLD prediction model from a large population cohort.We aimed to explore machine learning tools in predicting NAFLD.Methods:A retrospective cross-sectional study was performed on 15315 Chinese subjects(10373 training and 4942 testing sets).Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive models.Nine evaluation indicators including area under the receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),accuracy,positive predictive value,sensitivity,F1 score,Matthews correlation coefficient(MCC),specificity and negative prognostic value were applied to compare the performance among the models.The selected clinical and biochemical factors were ranked according to the importance in prediction ability.Results:Totally 4018/10373(38.74%)and 1860/4942(37.64%)subjects had ultrasound-proven NAFLD in the training and testing sets,respectively.Seven machine learning based models were developed and demonstrated good performance in predicting NAFLD.Among these models,the XGBoost model revealed the highest AUROC(0.873),AUPRC(0.810),accuracy(0.795),positive predictive value(0.806),F1 score(0.695),MCC(0.557),specificity(0.909),demonstrating the best prediction ability among the built models.Body mass index was the most valuable indicator to predict NAFLD according to the feature ranking scores.Conclusions:The XGBoost model has the best overall prediction ability for diagnosing NAFLD.The novel machine learning tools provide considerable beneficial potential in NAFLD screening.

Polymer Vesicles with Upper Critical Solution Temperature for Near-infrared Light-triggered Transdermal Delivery of Metformin in Diabetic Rats

Near-infrared light(NIR)triggered transdermal drug delivery systems are of great interest due to their on-demand drug release,which enable to enhance drug treatment efficiency as well as reduce side effect.Herein,a NIR-triggered microneedle(MN)patch array has been fabricated through depositing the photothermal conversion agent and anti-diabetic drug-loaded polymer vesicles with upper critical solution temperature(UCST)into dissolvable polymer matrix.The UCST-type polymer has a clearing point temperature of 41℃ and the drug-loaded polymer vesicles present excellent NIR-triggered and temperature responsive drug release behavior in vitro due to the disassociation of polymer vesicles upon NIR irradiation.After applying MNs to diabetic rats,significant hypoglycemic effect is achieved upon interval NIR irradiation and the blood glucose concentration can decrease to normal state for several hours,which enables to achieve the goal of on-demand drug release.This work suggests that the NIR-triggered MN drug release device has a potential application in the treatment of diabetes,especially for those requiring an active drug release manner.