Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Incidence of liver complications with hemochromatosis-associated HFE p.C282Y homozygosity: The role of central adiposity

BACKGROUND AND AIMS

The HFE p.C282Y+/+ (homozygous) genotype and central adiposity both increase liver disease and diabetes risks, but the combined effects are unclear. We estimated waist-to-hip ratio (WHR) associations with incident clinical outcomes in routine care in p.C282Y+/+ participants in the UK Biobank community cohort.

APPROACH AND RESULTS

Baseline WHR data available in 1297 male and 1602 female p.C282Y+/+ with 13.3-year mean follow-up for diagnoses. Spline regressions and Cox proportional hazard models were adjusted for age and genetic principal components. Cumulative incidence was from age 40 to 80 years. In p.C282Y+/+ males, there were positive linear WHR relationships for hospital inpatient-diagnosed liver fibrosis/cirrhosis ( p = 2.4 × 10 -5 ), liver cancer ( p = 0.007), non-alcoholic fatty liver disease ( p = 7.7 × 10 -11 ), and type 2 diabetes ( p = 5.1 × 10 -16 ). The hazard ratio for high WHR in p.C282Y+/+ males (≥0.96; 33.9%) was 4.13 for liver fibrosis/cirrhosis (95% CI: 2.04-8.39, p = 8.4 × 10 -5 vs. normal WHR); cumulative age 80 incidence 15.0% (95% CI: 9.8%-22.6%) versus 3.9% (95% CI: 1.9%-7.6%); for liver cancer, cumulative incidence was 9.2% (95% CI: 5.7%-14.6%) versus 3.6% (95% CI: 1.9%-6.6%). Hemochromatosis was diagnosed in 23 (96%) of the 24 high WHR p.C282Y+/+ males with incident fibrosis/cirrhosis. High WHR (≥0.85; 30.0%) p.C282Y+/+ females had raised hazards for liver fibrosis/cirrhosis (hazard ratio = 9.17, 95% CI: 2.51-33.50, p = 3.8 × 10 -7 ) and Non-alcoholic fatty liver disease (hazard ratio = 5.17, 95% CI: 2.48-10.78, p = 1.2 × 10 -5 ). Fibrosis/cirrhosis associations were similar in the subset with additional primary care diagnoses.

CONCLUSIONS

In p.C282Y+/+ males and females, increasing WHR is associated with substantially higher risks of liver complications. Interventions to reduce central adiposity to improve these outcomes should be tested.

An optimized instrument variable selection approach to improve causality estimation in association studies

Mendelian randomization (MR) is an emerging tool for inferring causality in genetic epidemiology. MR studies suffer bias from weak genetic instrument variables (IVs) and horizontal pleiotropy. We introduce a robust integrative framework strictly adhering with STROBE-MR guidelines to improve causality inference through MR studies. We implemented novel t-statistics-based criteria to improve the reliability of selected IVs followed by various MR methods. Further, we include sensitivity analyses to remove horizontal-pleiotropy bias. For functional validation, we perform enrichment analysis of identified causal SNPs. We demonstrate effectiveness of our proposed approach on 5 different MR datasets selected from diverse populations. Our pipeline outperforms its counterpart MR analyses using default parameters on these datasets. Notably, we found a significant association between total cholesterol and coronary artery disease (P = 1.16 × 10-71) in a single-sample dataset using our pipeline. Contrarily, this same association was deemed ambiguous while using default parameters. Moreover, in a two-sample dataset, we uncover 13 new causal SNPs with enhanced statistical significance (P = 1.06 × 10-11) for liver-iron-content and liver-cell-carcinoma. Likewise, these SNPs remained undetected using the default parameters (P = 7.58 × 10-4). Furthermore, our analysis confirmed previously known pathways, such as hyperlipidemia in heart diseases and gene ME1 in liver cancer. In conclusion, we propose a robust and powerful framework to infer causality across diverse populations and easily adaptable to different diseases.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk

Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.

Using Mendelian Randomization to Study the Role of Iron in Health and Disease

Iron has been shown to play a dual role in health and disease, with either a protective or harmful effect. Some of the contradictory findings from observational studies may be due to reverse causation, residual confounding, or small sample size. One approach that may overcome these limitations without the high cost of randomized control trials is the use of Mendelian randomization to examine the long-term role of iron in a variety of health outcomes. As there is emerging evidence employing Mendelian randomization as a method of assessing the role of micronutrients in health and disease, this narrative review will highlight recent Mendelian randomization findings examining the role of iron in cardiometabolic disorders, inflammation, neurological disorders, different cancers, and a number of other health-related outcomes.

Haemochromatosis

Haemochromatosis is one of the most common genetic diseases affecting patients of northern European ancestry. It is overdiagnosed in patients without iron overload and is underdiagnosed in many patients. Early diagnosis by genetic testing and therapy by periodic phlebotomy can prevent the most serious complications, which include liver cirrhosis, liver cancer, and death. This Seminar includes an update on the origins of haemochromatosis; and an overview pathophysiology, genetics, natural history, signs and symptoms, differential diagnoses, treatment with phlebotomy, outcomes, and future directions.

Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model

PURPOSE

To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features.

METHODS

One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models.

RESULTS

The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05).

CONCLUSIONS

It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.

Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots

For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

LncXIST Facilitates Iron Overload and Iron Overload-Induced Islet Beta Cell Injury in Type 2 Diabetes through miR-130a-3p/ALK2 Axis

Iron overload is directly associated with diabetes mellitus, loss of islet beta cell, and insulin resistance. Likewise, long noncoding RNA (lncRNA) is associated with type 2 diabetes (T2D). Moreover, lncRNAs could be induced by iron overload. Therefore, we are going to explore the molecular mechanism of lncRNA XIST in iron overload-related T2D. Real-time quantitative PCR and Western blot were used to detect gene and protein levels, respectively. TUNEL and MTT assay were performed to examine cell survival. The glucose test strip, colorimetric analysis kit, ferritin ELISA kit, and insulin ELISA kit were performed to examine the levels of glycolic, iron, and total iron-binding capacity, ferritin, and insulin in serum. Fluorospectrophotometry assay was used to examine labile iron pool level. XIST was higher expressed in T2D and iron overload-related T2D rat tissues and cells, and iron overload-induced promoted XIST expression in T2D. Higher XIST expression was associated with iron overload in patients with T2D. Knockdown of XIST alleviated iron overload and iron overload-induced INS-1 cells injury. Further, we found that XIST can sponge miR-130a-3p to trigger receptor-like kinase 2 (ALK2) expression. Moreover, knockdown of ALK2 alleviated iron overload and iron overload-induced INS-1 cells injury by inhibiting bone morphogenetic protein 6 (BMP6)/ALK2/SMAD1/5/8 axis but reversed with XIST upregulation, which was terminally boosted by overexpression of miR-130a-3p. XIST has the capacity to promote iron overload and iron overload-related T2D initiation and development through inhibition of ALK2 expression by sponging miR-130a-3p, and that targeting this axis may be an effective strategy for treating patients with T2D.

A rare genetic variant in the manganese transporter SLC30A10 and elevated liver enzymes in the general population

BACKGROUND

A genetic variant in the manganese transporter SLC30A10 (rs188273166, p.Thr95Ile) was associated with increased plasma alanine transaminase (ALT) in a recent genome-wide association study in the UK Biobank (UKB). The aims of the present study were to test the association of rs188273166 with ALT in an independent cohort, and to begin to assess the clinical, hepatic, and biochemical phenotypes associated with the variant.

METHODS

We included n = 334,886 white participants from UKB, including 14,462 with hepatic magnetic resonance imaging (MRI), and n = 113,612 individuals from the Copenhagen City Heart Study and the Copenhagen General Population Study combined.

RESULTS

Genotyping SLC30A10 p.Thr95Ile identified 816 heterozygotes in the UKB and 111 heterozygotes in the Copenhagen cohort. Compared to noncarriers, heterozygotes had 4 and 5 U/L higher levels of ALT in the UKB and Copenhagen cohort, respectively, and 3 U/L higher plasma aspartate transaminase and gamma-glutamyl transferase in the UKB. Heterozygotes also had higher corrected T1 on liver MRI, a marker of hepatic inflammation (p = 4 × 10-7), but no change in MRI-quantified steatosis (p = 0.57). Plasma manganese was within the normal range in nine heterozygotes that provided new blood samples. SLC30A10 p.Thr95Ile heterozygotes had an eightfold increased risk of biliary tract cancer in UKB (p = 4 × 10-7), but this association was not replicated in the Copenhagen cohort.

CONCLUSIONS

SLC30A10 p.Thr95Ile was associated with elevated liver enzymes in two large general population cohorts, and with MRI-quantified hepatic inflammation. A rare genetic variant (p.Thr95Ile) in the manganese transporter SLC30A10 is associated with elevated plasma alanine transaminase (ALT) and higher corrected T1 on liver MRI, markers of liver inflammation. These data support that the variant may increase the risk of liver disease.

Distribution and Associated Factors of Hepatic Iron-A Population-Based Imaging Study

Hepatic iron overload can cause severe organ damage; therefore, an early diagnosis and the identification of potential risk factors is crucial. We aimed to investigate the sex-specific distribution of hepatic iron content (HIC) in a population-based cohort and identify relevant associated factors from a panel of markers. We analyzed N = 353 participants from a cross-sectional sample (KORA FF4) who underwent whole-body magnetic resonance imaging. HIC was assessed by single-voxel spectroscopy with a high-speed T2-corrected multi-echo technique. A large panel of markers, including anthropometric, genetic, and laboratory values, as well as behavioral risk factors were assessed. Relevant factors associated with HIC were identified by variable selection based on LASSO regression with bootstrap resampling. HIC in the study sample (mean age at examination: 56.0 years, 58.4% men) was significantly lower in women (mean ± SD: 39.2 ± 4.1 s^-1) than in men (41.8 ± 4.7 s^-1, p < 0.001). Relevant factors associated with HIC were HbA1c as well as prediabetes for men and visceral adipose tissue as well as age for women. Hepatic fat, alcohol consumption, and genetic risk score for iron levels were associated with HIC in both sexes. In conclusion, there are sex-specific associations of HIC with markers of body composition, glucose metabolism, and alcohol consumption.

MRI-Based Iron Phenotyping and Patient Selection for Next-Generation Sequencing of Non-Homeostatic Iron Regulator Hemochromatosis Genes

BACKGROUND AND AIMS

High serum ferritin is frequent among patients with chronic liver disease and commonly associated with hepatic iron overload. Genetic causes of high liver iron include homozygosity for the p.Cys282Tyr variant in homeostatic iron regulator (HFE) and rare variants in non-HFE genes. The aims of the present study were to describe the landscape and frequency of mutations in hemochromatosis genes and determine whether patient selection by noninvasive hepatic iron quantification using MRI improves the diagnostic yield of next-generation sequencing (NGS) in patients with hyperferritinemia.

APPROACH AND RESULTS

A cohort of 410 unselected liver clinic patients with high serum ferritin (defined as ≥200 μg/L for women and ≥300 μg/L for men) was investigated by HFE genotyping and abdominal MRI R2*. Forty-one (10%) patients were homozygous for the p.Cys282Tyr variant in HFE. Of the remaining 369 patients, 256 (69%) had high transferrin saturation (TSAT; ≥45%) and 199 (53%) had confirmed hepatic iron overload (liver R2* ≥70 s^-1 ). NGS of hemochromatosis genes was carried out in 180 patients with hepatic iron overload, and likely pathogenic variants were identified in 68 of 180 (38%) patients, mainly in HFE (79%), ceruloplasmin (25%), and transferrin receptor 2 (19%). Low spleen iron (R2* <50 s^-1 ), but not TSAT, was significantly associated with the presence of mutations. In 167 patients (93%), no monogenic cause of hepatic iron overload could be identified.

CONCLUSIONS

In patients without homozygosity for p.Cys282Tyr, coincident pathogenic variants in HFE and non-HFE genes could explain hyperferritinemia with hepatic iron overload in a subset of patients. Unlike HFE hemochromatosis, this type of polygenic hepatic iron overload presents with variable TSAT. High ferritin in blood is an indicator of the iron storage disease, hemochromatosis. A simple genetic test establishes this diagnosis in the majority of patients affected. MRI of the abdomen can guide further genetic testing.

Genetic Support of A Causal Relationship Between Iron Status and Type 2 Diabetes: A Mendelian Randomization Study

CONTEXT

Iron overload is a known risk factor for type 2 diabetes (T2D); however, iron overload and iron deficiency have both been associated with metabolic disorders in observational studies.

OBJECTIVE

Using mendelian randomization (MR), we assessed how genetically predicted systemic iron status affected T2D risk.

METHODS

A 2-sample MR analysis was used to obtain a causal estimate. We selected genetic variants strongly associated (P < 5 × 10-8) with 4 biomarkers of systemic iron status from a study involving 48 972 individuals performed by the Genetics of Iron Status consortium and applied these biomarkers to the T2D case-control study (74 124 cases and 824 006 controls) performed by the Diabetes Genetics Replication and Meta-analysis consortium. The simple median, weighted median, MR-Egger, MR analysis using mixture-model, weighted allele scores, and MR based on a Bayesian model averaging approaches were used for the sensitivity analysis.

RESULTS

Genetically instrumented serum iron (odds ratio [OR]: 1.07; 95% CI, 1.02-1.12), ferritin (OR: 1.19; 95% CI, 1.08-1.32), and transferrin saturation (OR: 1.06; 95% CI, 1.02-1.09) were positively associated with T2D. In contrast, genetically instrumented transferrin, a marker of reduced iron status, was inversely associated with T2D (OR: 0.91; 95% CI, 0.87-0.96).

CONCLUSION

Genetic evidence supports a causal link between increased systemic iron status and increased T2D risk. Further studies involving various ethnic backgrounds based on individual-level data and studies regarding the underlying mechanism are warranted for reducing the risk of T2D.

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8-44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.

Ethnic Differences in Iron Status

Iron is unique among all minerals in that humans have no regulatable excretory pathway to eliminate excess iron after it is absorbed. Iron deficiency anemia occurs when absorbed iron is not sufficient to meet body iron demands, whereas iron overload and subsequent deposition of iron in key organs occur when absorbed iron exceeds body iron demands. Over time, iron accumulation in the body can increase risk of chronic diseases, including cirrhosis, diabetes, and heart failure. To date, only ∼30% of the interindividual variability in iron absorption can be captured by iron status biomarkers or iron regulatory hormones. Much of the regulation of iron absorption may be under genetic control, but these pathways have yet to be fully elucidated. Genome-wide and candidate gene association studies have identified several genetic variants that are associated with variations in iron status, but the majority of these data were generated in European populations. The purpose of this review is to summarize genetic variants that have been associated with alterations in iron status and to highlight the influence of ethnicity on the risk of iron deficiency or overload. Using extant data in the literature, linear mixed-effects models were constructed to explore ethnic differences in iron status biomarkers. This approach found that East Asians had significantly higher concentrations of iron status indicators (serum ferritin, transferrin saturation, and hemoglobin) than Europeans, African Americans, or South Asians. African Americans exhibited significantly lower hemoglobin concentrations compared with other ethnic groups. Further studies of the genetic basis for ethnic differences in iron metabolism and on how it affects disease susceptibility among different ethnic groups are needed to inform population-specific recommendations and personalized nutrition interventions for iron-related disorders.

Whole Genome Interpretation for a Family of Five

Although best practices have emerged on how to analyse and interpret personal genomes, the utility of whole genome screening remains underdeveloped. A large amount of information can be gathered from various types of analyses via whole genome sequencing including pathogenicity screening, genetic risk scoring, fitness, nutrition, and pharmacogenomic analysis. We recognize different levels of confidence when assessing the validity of genetic markers and apply rigorous standards for evaluation of phenotype associations. We illustrate the application of this approach on a family of five. By applying analyses of whole genomes from different methodological perspectives, we are able to build a more comprehensive picture to assist decision making in preventative healthcare and well-being management. Our interpretation and reporting outputs provide input for a clinician to develop a healthcare plan for the individual, based on genetic and other healthcare data.

Multiparametric MR mapping in clinical decision-making for diffuse liver disease

Accurate diagnosis, monitoring and treatment decisions in patients with chronic liver disease currently rely on biopsy as the diagnostic gold standard, and this has constrained early detection and management of diseases that are both varied and can be concurrent. Recent developments in multiparametric magnetic resonance imaging (mpMRI) suggest real potential to bridge the diagnostic gap between non-specific blood-based biomarkers and invasive and variable histological diagnosis. This has implications for the clinical care and treatment pathway in a number of chronic liver diseases, such as haemochromatosis, steatohepatitis and autoimmune or viral hepatitis. Here we review the relevant MRI techniques in clinical use and their limitations and describe recent potential applications in various liver diseases. We exemplify case studies that highlight how these techniques can improve clinical practice. These techniques could allow clinicians to increase their arsenals available to utilise on patients and direct appropriate treatments.

Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis

BACKGROUND & AIMS

MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases.

METHODS

First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures.

RESULTS

We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p <5×10-8). Four of the variants (rs759359281 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated aminotransferases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and body mass index were causally associated with elevated cT1, whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective.

CONCLUSION

The association between 2 metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at-risk individuals.

LAY SUMMARY

We estimated levels of liver inflammation and scarring based on magnetic resonance imaging of 14,440 UK Biobank participants. We performed a genetic study and identified variations in 6 genes associated with levels of liver inflammation and scarring. Participants with variations in 4 of these genes also had higher levels of markers of liver cell injury in blood samples, further validating their role in liver health. Two identified genes are involved in the transport of metal ions in our body. Further investigation of these variations may lead to better detection, assessment, and/or treatment of liver inflammation and scarring.