Association of serum ferritin with non-alcoholic fatty liver disease: a meta-analysis

Du, Shui Xian; Lü, Lin; Geng, Ning; Victor, David W.; Chen, Li Zhen; Yue, Hai Yan; Xin, Yong; Xuan, Shi Ying; Jin, Wen

doi:10.1186/s12944-017-0613-4

Cited by 47 publications

(37 citation statements)

References 35 publications

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“…The results of the systematic search and selection of eligible studies are presented in Figure 1. Overall, 3,564 articles were searched and 10 meta-analyses published from 2014 to 2020 (13,14,(28)(29)(30)(31)(32)(33)(34)(35) were eligible. The median number of studies per meta-analysis included was 13 (range, 7-30).…”

Section: Search Resultsmentioning

confidence: 99%

Associations of Nutritional, Lifestyle, and Metabolic Factors With Non-alcoholic Fatty Liver Disease: An Umbrella Review With More Than 380,000 Participants

Xia

et al. 2021

Front. Nutr.

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Background & Aims: Nonalcoholic fatty liver disease (NAFLD) is the most common liver injury. We performed this umbrella review of meta-analyses to summarize the evidence on the associations of nutritional, lifestyle, and metabolic factors with NAFLD.Methods: We searched the PubMed, Embase, and Web of Science databases from inception until July 2, 2020, to identify meta-analyses of observational studies which explored the associations of nutritional, lifestyle, and metabolic factors with NAFLD. Evidence levels were assessed using summary effect sizes, 95% prediction intervals, between-study heterogeneity, evidence of small-study effects, and evidence of excess significance bias for each meta-analysis. (No. of PROSPERO, CRD42020200124).Results: Twenty two risk or protective factors from 10 published meta-analyses were included and studied. Three risk factors (sugar-sweetened beverage consumption, serum fetuin-A, and waist circumference) with highly suggestive levels of evidence and three risk factors (soft drink consumption, former smoking, and body mass index) with suggestive levels of evidence were identified. Only two protective factors (physical activity and serum vitamin D level [among adults in Western countries]) with suggestive levels of evidence were identified. Furthermore, other six risk factors and two protective factors with weak levels of evidence were identified.Conclusions: We found varying levels of evidence of associations of nutritional, lifestyle, and metabolic factors and NAFLD. The results suggest that nutritional and lifestyle management should be considered as a major primary preventive strategy for NAFLD. Moreover, considering the low quality of included meta-analyses and limited area of research topics, future high-quality original studies and meta-analyses should be performed to study these associations.

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Section: Search Resultsmentioning

confidence: 99%

Associations of Nutritional, Lifestyle, and Metabolic Factors With Non-alcoholic Fatty Liver Disease: An Umbrella Review With More Than 380,000 Participants

Xia

et al. 2021

Front. Nutr.

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“…Although it remains unclear whether ferritin is the cause or consequence of NASH, its association with direct injury in the liver has been demonstrated, and the severity of the histological lesions in both NASH and NAFLD has been predicted. Thus, this can be a useful biomarker for identifying patients at a high risk for NASH [ 55 , 56 ].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

García-Carretero

Holgado-Cuadrado

Barquero-Pérez

2021

Entropy

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Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann–Whitney–Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.

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“…The interesting point is that Asian people, compared with European people, may have a natural selection for the hemochromatosis gene, which would potentiate iron absorption. 12 Hyperferritinemia is an index of iron overload but can be induced by acute inflammation, 13 liver disease, 14 and type 2 diabetes. 15 In this study, our fully adjusted model included patients with diabetes and markers of malnutrition/ inflammation such as leukocyte count, albumin level, Figure 2.…”

Section: Discussionmentioning

confidence: 99%

Different Effects of Iron Indices on Mortality in Patients With Autosomal Dominant Polycystic Kidney Disease After Long-Term Hemodialysis: A Nationwide Population-Based Study

Yeh

Lin

Hong

et al. 2019

Journal of Renal Nutrition

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Objective: Iron supplementation and erythropoietin stimulating agents (ESAs) are essential for maintaining hemoglobin levels in hemodialysis patients. However, patients with autosomal-dominant polycystic kidney disease (PKD) have higher endogenous erythropoietin levels, so their recommended iron indices for hemodialysis patients may differ. This study evaluated iron profiles, including ferritin levels and transferrin saturation (TSAT) to identify factors affecting mortality in patients on dialysis, and those associated with mortality in patients with and without PKD. Design: This cohort study from the Taiwan Renal Registry Data System stratified mortality risk by the presence of PKD recorded as the underlying disease. Subjects: We enrolled 1346 hemodialysis patients with PKD and 82,873 hemodialysis patients without PKD. Main outcome measure: The primary outcome was 3-year all-cause mortality. Predictors included time-averaged and baseline serum ferritin levels and TSAT. Multivariate Cox regression analysis adjusting for age, comorbidities, and relevant laboratory parameters was used to estimate the all-cause hazard ratios (HRs) for mortality. Results: The mean ages of patients with and without PKD were 56.2613.2 and 61.7613.5 years and the median follow-up time was 37 (15-76) months. The adjusted mortality risks for time-averaged ferritin levels .800 ng/mL (HR51.52; 95% confidence interval: 1.40-1.65) or TSAT levels .50% (HR51.46; 95% confidence interval: 1.30-1.65) were significantly higher among patients without PKD than those for patients with normal iron indices. However, a U-shaped curve of mortality against ferritin/TSAT levels was not observed in patients with PKD. In the sensitivity test, there was no difference among PKD patients who underwent regular ESA therapy and those who did not. Conclusion: Iron indices have different effects on mortality among patients with and without PKD. Iron supplementation, recommended serum ferritin levels, or TSAT should be monitored in hemodialysis patients, especially those without PKD. Clinicians should consider treating anemia in hemodialysis patients individually, especially in PKD.

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Association of serum ferritin with non-alcoholic fatty liver disease: a meta-analysis

Cited by 47 publications

References 35 publications

Associations of Nutritional, Lifestyle, and Metabolic Factors With Non-alcoholic Fatty Liver Disease: An Umbrella Review With More Than 380,000 Participants

Associations of Nutritional, Lifestyle, and Metabolic Factors With Non-alcoholic Fatty Liver Disease: An Umbrella Review With More Than 380,000 Participants

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

Different Effects of Iron Indices on Mortality in Patients With Autosomal Dominant Polycystic Kidney Disease After Long-Term Hemodialysis: A Nationwide Population-Based Study

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