Association between iron metabolism and non-alcoholic fatty liver disease: results from the National Health and Nutrition Examination Survey (NHANES 2017-2018) and a controlled animal study

Association between hemoglobin and non-alcoholic fatty liver disease (NAFLD) in United States adults: Results from NHANES 2017-2020

Background

Non-alcoholic fatty liver disease (NAFLD), a chronic liver condition of increasing prevalence, is closely related to various metabolic disorders. Hemoglobin, a protein that transports oxygen in red blood cells, is the focus of this study, which seeks to investigate its potential association with NAFLD.

Methods

We selected 6,516 eligible adult participants from the United States using the 2017-2020 National Health and Nutrition Examination Survey database for cross-sectional analyses. We analyzed the association of hemoglobin with NAFLD using weighted logistic regression models.

Results

The study performed a weighted logistic regression modeling analysis, which verified that hemoglobin levels were positively associated with NAFLD, especially in the higher hemoglobin quartile groups. Subgroup analyses revealed no significant interactions, demonstrating the robustness of the model. The analysis of mediation effects showed that Gamma-Glutamyl Transferase, Alanine Aminotransferase, and triglycerides were important mediating variables in the relationship between hemoglobin and NAFLD.

Conclusion

Increased hemoglobin levels were found to be significantly and independently associated with an increased NAFLD risk. This insight is crucial for the risk assessment and early detection of NAFLD, underscoring the need for heightened vigilance in individuals with higher hemoglobin levels.

Effects of liver's state of health on its iron and plutonium content

PURPOSE

Plutonium and iron share a common metabolism in terms of their transportation and accumulation in the human body. This study examined their concentrations in livers with different states of health, and the effects of fatty degeneration and cirrhosis on their accumulation in the liver.

MATERIALS AND METHODS

We determined the concentrations of plutonium and iron in autopsy liver samples from 1976-1979. Using statistical analysis, we investigated the relationships between the different variables.

RESULTS AND CONCLUSIONS

The burdens of 239,240Pu and Fe correlated positively (Rs = 0.411) in the healthy livers, but not in the livers that had pathological findings. In contrast to the Fe content, the 239,240Pu content in the fatty degenerated or cirrhotic livers was significantly lower than that in normal livers. This difference may suggest that plutonium and iron do not accumulate or are not excreted in the same way in fatty degenerated and cirrhotic livers. The reaction mechanisms for the binding and excretion of plutonium, particularly in a fatty degenerated liver, are not yet fully known.

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Iron Metabolic Biomarkers and the Mortality Risk in the General Population: A Nationwide Population-Based Cohort Study

Context

Iron is an essential element in the human body and plays a critical role in many physiological and cellular processes. However, the association between iron status and the risk of all-cause or cause-specific mortality has not been well-investigated. And it is unclear whether the association between iron metabolic biomarkers and the risk of mortality differs between people with and without diabetes mellitus (DM).

Objective

This work aimed to investigate associations between iron metabolic biomarkers and all-cause and cause-specific mortality risk in the general population, and heterogeneities in the associations among population with and without DM..

Methods

A total of 29 166 adults from the National Health and Nutrition Examination Survey (NHANES) III and NHANES 1999 to 2010 were included, with linkage to the National Death Index to December 31, 2019. Cox proportional-hazard models and Fine-Gray subdistribution hazard models were used to estimate associations between iron metabolic biomarkers and outcomes.

Results

During a median follow-up of 18.83 years, 9378 deaths were observed, including 3420 cardiovascular disease (CVD) deaths and 1969 cancer deaths. A significant linear association between serum ferritin (SF) and all-cause mortality was observed among the overall population and those without DM. J-shaped associations between transferrin saturation (TSAT) and all-cause and CVD mortality were observed among all populations. In the overall population, compared to the first quartile (Q1) group, the adjusted hazard ratio (HR) (95% CI) for all-cause mortality was 1.07 (1.00-1.15), 1.05 (0.98-1.12), 1.13 (1.05-1.21) in Q2, Q3, and Q4 groups for SF, while the HR was 0.94 (0.88-0.99), 0.92 (0.86-0.97), and 0.93 (0.88-0.99) for TSAT. In individuals without DM, the adjusted HR of the Q4 of SF were 1.19 (1.03-1.37) for CVD mortality and 1.25 (1.05-1.48) for cancer mortality. In individuals with DM, the adjusted HRs of the Q4 of TSAT were 0.76 (0.62-0.93) for CVD mortality and 1.47 (1.07-2.03) for cancer mortality.

Conclusion

Iron metabolism abnormalities increase mortality risk in the general population. The associations of iron status with mortality were significantly different between individuals with and without DM, which indicated tailored strategies for iron homeostasis are needed.

Association between circulating biomarkers and non-alcoholic fatty liver disease: An integrative Mendelian randomization study of European ancestry

BACKGROUND AND AIM

Circulating biomarkers provide potential diagnostic or prognostic information on disease presentation, progression or both. Early detection of circulating risk biomarkers is critical for non-alcoholic fatty liver disease (NAFLD) prevention. We aimed to systematically assess the potential causal relationship of genetically predicted 60 circulatory biomarkers with NAFLD using a two-sample Mendelian randomization (MR) design.

METHODS AND RESULTS

We extracted instrumental variables for 60 circulating biomarkers, and obtained genome-wide association data for NAFLD from 3 sources [(including Anstee, FinnGen and UK Biobank (N ranges: 19264-377988)] among individuals of European ancestry. Our primary method was inverse-variance weighted (IVW) MR, with a series of additional and sensitivity analyses to test the hypothesis of MR. MR results showed that genetically predicted higher density lipoprotein-cholesterol (odds ratio (OR) = 0.86, 95% confidence interval (CI): 0.77-0.96) and vitamin D (OR = 0.39, 95% CI: 0.19-0.78) levels decreased the risk of NAFLD, whereas genetically predicted higher alanine (OR = 1.68, 95% CI: 1.21-2.33), histidine (OR = 1.21, 95% CI: 1.00-1.46), lactate (OR = 2.64, 95% CI: 1.09-6.39), triglycerides (OR = 1.16, 95% CI: 1.05-1.13), ferritin (OR = 1.17, 95% CI: 1.01-1.37), serum iron (OR = 1.23, 95% CI: 1.07-1.41) and transferrin saturation (OR = 1.16, 95% CI: 1.05-1.29), component 4 (OR = 1.10, 95% CI: 1.01-1.20), interleukin-1 receptor antagonist (OR = 1.12, 95% CI: 1.04-1.21) and interleukin-6 (OR = 1.62, 95% CI: 1.14-2.30) levels increased the risk of NAFLD.

CONCLUSIONS

The findings might aid in elucidating the underlying processes of these causal relationships and provide strong evidence for focusing on high-risk populations and the therapeutic management of specific biomarkers.

The association of systemic inflammatory biomarkers with non-alcoholic fatty liver disease: a large population-based cross-sectional study

Purpose

The aim of this study was to explore the relationship between non-alcoholic fatty liver disease (NAFLD) and the two blood inflammatory markers including the systemic immune-inflammation (SII) index, and the system inflammation response index (SIRI).

Methods

The National Health and Nutrition Examination Survey data between the year of 2017-2018 was used for this cross-sectional study. In order to analyze the association of SII index, and SIRI and risk of NAFLD, we used multivariable logistic regression models, restricted cubic spline (RCS) plot, and subgroup analysis to analyze the data.

Results

In total, there were 1,199 individuals who participated in the survey. As shown by the RCS plot, SII index, and SIRI were linked with NAFLD risk in a U-shaped pattern. With regard to known confounding variables, when comparing the lowest quartile, the odds ratio with 95 % confidence interval for prevalence of NAFLD across the quartiles of SII index and SIRI were (0.89 (0.57, 1.41), 0.56 (0.35, 0.89) and 1.01 (0.64, 1.59)), and (0.77 (0.48, 1.23), 0.79 (0.50, 1.24) and 0.94 (0.60, 1.47)), respectively. Additionally, SII index, and SIRI and NAFLD risk also were U-curve correlated among the participants in age ≥60 years, female, without hypertension, and BMI of ≥30 kg/m2.

Conclusions

There was a U-shaped association of SII index and SIRI with prevalence of NAFLD, indicating that SII index and SIRI should be monitored dynamically.

Association of Systemic Immune-Inflammation Index with Non-Alcoholic Fatty Liver Disease: A Population-Based Cross-Sectional Study

Background

The aim of this study was to explore the relationship between systemic immune-inflammation (SII) index with non-alcoholic fatty liver disease (NAFLD) in the general population of the United States (U.S.).

Methods

We conducted a cross-sectional study of subjects in the National Health and Nutrition Examination Survey 2017-2018. For the analysis of the association between SII index and risk of NAFLD, the restricted cubic spline (RCS) plot, we performed multivariable logistic regression models and subgroup analysis. In addition, generalized additive models with smooth functions were conducted for the relationship between the SII index and the ZJU index, the BARD score, and the NAFLD fibrosis score.

Results

There were a total of 1197 individuals in our study. Taking into account known confounding variables, compared with the lowest quartiles, the odds ratios with 95% confidence intervals for NAFLD across the quartiles were 0.923 (0.585, 1.455), 0.563 (0.351, 0.901), and 1.061 (0.669, 1.682), respectively. As shown by the RCS plot, the SII index was linked with NAFLD risk in a U-shaped pattern. Based on the results of subgroup analysis, SII index and NAFLD risk were U-curve correlated among participants in all age groups, male or female, with or without hypertension, with diabetes mellitus, and with a BMI of <30 or >30 kg/m2. The SII index was linearly positive with the ZJU index but negative with the NAFLD fibrosis score. However, the SII index and BARD score showed a trend of first decreasing, then increasing, and then decreasing.

Conclusion

The U-shaped relationships exist between SII index and risk of NAFLD, which highlighted that we should focus on the dynamic change of SII index.