Dataset Information

The association between Helicobacter pylori with nonalcoholic fatty liver disease assessed by controlled attenuation parameter and other metabolic factors.

ABSTRACT:

Aim

Existing studies have suggested an association between Helicobacter pylori (Hp) infection and nonalcoholic fatty liver disease (NAFLD). We investigated the relationship between Hp infection and NAFLD using controlled attenuation parameter (CAP) and other metabolic factors.

Method

We conducted a retrospective cohort study of apparently healthy individuals who underwent liver Fibroscan during health screening tests between January 2018 and December 2018. Diagnosis of Hp infection was based on a serum anti-Hp IgG antibody test and CAP values were used to diagnose NAFLD.

Results

Among the 1,784 subjects (mean age 55.3 years, 83.1% male), 708 (39.7%) subjects showed positive results of Hp serology. In the multivariate analysis, obesity (body mass index ≥25) (odds ratio [OR] 3.44, 95% confidence interval [CI] 2.75-4.29), triglyceride (OR 2.31, 95% CI 1.80-2.97), and the highest tertile of liver stiffness measurement (OR 2.08, 95% CI 1.59-2.71) were found to be associated with NAFLD, defined by CAP ≥248 dB/m, while Hp-seropositivity showed no association with NAFLD. Serum levels of HDL cholesterol significantly decreased in subjects with Hp-seropositivity compared to HP-seronegativity in both groups with and without NAFLD (P<0.001).

Conclusion

While Hp seropositivity was not associated with CAP-defined NAFLD, serum HDL cholesterol level were negatively associated with Hp-seropositivity in both groups with and without NAFLD. Further clinical and experimental studies are necessary to determine the association between Hp infection and NAFLD.

SUBMITTER: Han YM

PROVIDER: S-EPMC8668115 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

The association between Helicobacter pylori with nonalcoholic fatty liver disease assessed by controlled attenuation parameter and other metabolic factors.

Han Yoo Min YM Lee Jooyoung J Choi Ji Min JM Kwak Min-Sun MS Yang Jong In JI Chung Su Jin SJ Yim Jeong Yoon JY Chung Goh Eun GE

PloS one 20211213 12

<h4>Aim</h4>Existing studies have suggested an association between Helicobacter pylori (Hp) infection and nonalcoholic fatty liver disease (NAFLD). We investigated the relationship between Hp infection and NAFLD using controlled attenuation parameter (CAP) and other metabolic factors.<h4>Method</h4>We conducted a retrospective cohort study of apparently healthy individuals who underwent liver Fibroscan during health screening tests between January 2018 and December 2018. Diagnosis of Hp infectio ...[more]

PMID: 34898613

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Project description:BackgroundNonalcoholic fatty liver disease (NAFLD) is recognized as one of the most common chronic liver diseases worldwide. This study aims to assess the efficacy of automated machine learning (AutoML) in the identification of NAFLD using a population-based cross-sectional database.MethodsAll data, including laboratory examinations, anthropometric measurements, and demographic variables, were obtained from the National Health and Nutrition Examination Survey (NHANES). NAFLD was defined by controlled attenuation parameter (CAP) in liver transient ultrasound elastography. The least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection. Six algorithms were utilized on the H2O-automated machine learning platform: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost), and Deep Learning (DL). These algorithms were selected for their diverse strengths, including their ability to handle complex, non-linear relationships, provide high predictive accuracy, and ensure interpretability. The models were evaluated by area under receiver operating characteristic curves (AUC) and interpreted by the calibration curve, the decision curve analysis, variable importance plot, SHapley Additive exPlanation plot, partial dependence plots, and local interpretable model agnostic explanation plot.ResultsA total of 4177 participants (non-NAFLD 3167 vs NAFLD 1010) were included to develop and validate the AutoML models. The model developed by XGBoost performed better than other models in AutoML, achieving an AUC of 0.859, an accuracy of 0.795, a sensitivity of 0.773, and a specificity of 0.802 on the validation set.ConclusionsWe developed an XGBoost model to better evaluate the presence of NAFLD. Based on the XGBoost model, we created an R Shiny web-based application named Shiny NAFLD (http://39.101.122.171:3838/App2/). This application demonstrates the potential of AutoML in clinical research and practice, offering a promising tool for the real-world identification of NAFLD.

Project description:BackgroundThe consistency in steatosis grading between magnetic resonance imaging-based proton density fat fraction (MRI-PDFF) and controlled attenuation parameter (CAP) before and after treatment remains unclear. This study aimed to compare the diagnostic accuracy of steatosis grading between MRI-PDFF and CAP using liver biopsy as standard and to evaluate the value of monitoring changes in steatosis grading with CAP during follow-up utilizing MRI-PDFF as a reference.MethodsConsecutive patients from a biopsy cohort and a randomized controlled trial were included in this study and classified into 3 groups (the biopsy, orlistat treatment, and routine treatment subgroups). Hepatic steatosis was measured via MRI-PDFF and CAP at baseline and at the 6th month; the accuracy and cutoffs were assessed in the liver biopsy cohort at baseline.ResultsA total of 209 consecutive patients were enrolled. MRI-PDFF and CAP showed comparable diagnostic accuracy for detecting pathological steatosis [⩾S1, area under the receiver operating characteristic curve (AUC) = 0.984 and 0.972, respectively]; in contrast, CAP presented significantly lower AUCs in grades S2-3 and S3 (0.820 and 0.815, respectively). The CAP values correlated well with the MRI-PDFF values at baseline and at the 6th month (r = 0.809 and 0.762, respectively, both p < 0.001), whereas a moderate correlation in their changes (r = 0.612 and 0.524 for moderate-severe and mild steatosis, respectively; both p < 0.001) was observed. The AUC of CAP change was obtained to predict MRI-PDFF changes of ⩾5% and ⩾10% (0.685 and 0.704, p < 0.001 and p = 0.001, respectively). The diagnostic agreement of steatosis grade changes between MRI-PDFF and CAP was weak (κ = 0.181, p = 0.001).ConclusionsCAP has decreased value for the initial screening of moderate-severe steatosis and is limited in monitoring changes in steatosis during treatment. The confirmation of steatosis grading with MRI-PDFF remains necessary.

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Dataset Information

The association between Helicobacter pylori with nonalcoholic fatty liver disease assessed by controlled attenuation parameter and other metabolic factors.

Aim

Method

Results

Conclusion

Publications

The association between Helicobacter pylori with nonalcoholic fatty liver disease assessed by controlled attenuation parameter and other metabolic factors.

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