Genome-Wide and Abdominal MRI Data Provide Evidence That a Genetically Determined Favorable Adiposity Phenotype Is Characterized by Lower Ectopic Liver Fat and Lower Risk of Type 2 Diabetes, Heart Disease, and Hypertension

Ji, Yingjie; Yiorkas, Andrianos M.; Frau, Francesca; Mook‐Kanamori, Dennis O.; Staiger, Harald; Thomas, E. Louise; Atabaki‐Pasdar, Naeimeh; Campbell, Archie; Tyrrell, Jessica; Jones, Samuel E.; Beaumont, Robin N; Wood, Andrew R.; Tuke, Marcus A.; Ruth, Katherine S.; Mahajan, Anubha; Murray, Anna; Freathy, Rachel M.; Weedon, Michael N.; Hattersley, Andrew T.; Hayward, Caroline; Machann, Jürgen; Häring, Hans Ulrich; Franks, Paul W.; Mutsert, Renée de; Pearson, Ewan R.; Stefan, Norbert; Frayling, Timothy M.; Allebrandt, Karla V.; Bell, Jimmy D.; Blakemore, Alexandra I. F.; Yaghootkar, Hanieh

doi:10.2337/db18-0708

Cited by 79 publications

(103 citation statements)

References 65 publications

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“…We found evidence of a causal association between insulin resistance (IVW p = 0.0001), non-alcoholic fatty liver (IVW p = 0.01), type 2 diabetes (IVW p = 0.004), BMI (IVW p = 0.002) and higher cT1. We also found evidence for a protective role of favourable adiposity variants (variants associated with higher adiposity but lower risk of cardiometabolic diseases and lower ectopic fat) 36 and cT1 (IVW p = 0.01) ( Table S6 ). Our analyses were robust across a range of sensitivity analyses ( Table S6 ).…”

Section: Resultsmentioning

confidence: 59%

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

Parisinos

Wilman

Thomas

et al. 2020

Journal of Hepatology

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108

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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.

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Section: Resultsmentioning

confidence: 59%

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

Parisinos

Wilman

Thomas

et al. 2020

Journal of Hepatology

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108

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“…Detailed descriptions of body composition and fat distribution could play an important part in furthering our understanding of metabolic disorders, but networking across disciplines is needed for a complete transition to precision medicine. For example, studies have investigated the links between body composition and genetics , and suggestions have been made on how to relate body composition to body function and metabolic processes .…”

Section: Discussionmentioning

confidence: 99%

Sub‐phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets

Linge¹,

Whitcher

Borga

et al. 2019

Obesity

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Objective This study performed individual‐centric, data‐driven calculations of propensity for coronary heart disease (CHD) and type 2 diabetes (T2D), utilizing magnetic resonance imaging‐acquired body composition measurements, for sub‐phenotyping of obesity and nonalcoholic fatty liver disease (NAFLD). Methods A total of 10,019 participants from the UK Biobank imaging substudy were included and analyzed for visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat. An adaption of the k‐nearest neighbors algorithm was applied to the imaging variable space to calculate individualized CHD and T2D propensity and explore metabolic sub‐phenotyping within obesity and NAFLD. Results The ranges of CHD and T2D propensity for the whole cohort were 1.3% to 58.0% and 0.6% to 42.0%, respectively. The diagnostic performance, area under the receiver operating characteristic curve (95% CI), using disease propensities for CHD and T2D detection was 0.75 (0.73‐0.77) and 0.79 (0.77‐0.81). Exploring individualized disease propensity, CHD phenotypes, T2D phenotypes, comorbid phenotypes, and metabolically healthy phenotypes were found within obesity and NAFLD. Conclusions The adaptive k‐nearest neighbors algorithm allowed an individual‐centric assessment of each individual’s metabolic phenotype moving beyond discrete categorizations of body composition. Within obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and consequently enable optimization of treatment.

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“…MR provides a means to uncouple the impact of higher adiposity from its often associated metabolic dysfunction by using genetic variants associated with ‘favourable adiposity’. The adiposity-increasing alleles at these loci have a paradoxical effect on adiposity and risk of cardiometabolic disease [ 96 – 98 ] and are associated with a favourable metabolic profile. Studies using MRI scans of abdominal fat suggest that the underlying mechanism involves an ability to store excess fat in a safe place (subcutaneous adipose tissue), which protects against fat accumulation in ectopic organs like the liver [ 98 ].…”

Section: Vertical Pleiotropy: Using Genetic Variants To Investigate Amentioning

confidence: 99%

Using genetics to decipher the link between type 2 diabetes and cancer: shared aetiology or downstream consequence?

Vincent

Yaghootkar

2020

Diabetologia

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Recent developments in the field of genetics have accelerated our understanding of the aetiology of complex diseases. Type 2 diabetes mellitus and cancer are no exception, with large-scale genome-wide association studies (GWAS) facilitating exploration of the underlying pathology. Here, we discuss how genetics studies can be used to investigate the relationship between these complex diseases. Observational epidemiological studies consistently report that people with type 2 diabetes have a higher risk of several types of cancer. Indeed, type 2 diabetes and cancer share many common risk factors, such as obesity, ageing, poor diet and low levels of physical activity. However, questions remain regarding the biological mechanisms that link these two diseases. Large-scale GWAS of type 2 diabetes and cancer allow us to consider the evidence for shared genetic architecture. Several shared susceptibility genes have been identified, yet tissue specificity and direction of effect must be taken into account when considering common genetic aetiology. We also consider how GWAS, and associated techniques such as Mendelian randomisation, allow us to dissect the link between the two diseases and address questions such as 'Does type 2 diabetes cause cancer or is the increased risk observed driven by higher adiposity or another associated metabolic feature?' Keywords Adiposity. Cancer. Diabetes. Genetics. GWAS. Mendelian randomisation. Review. Type 2 diabetes Abbreviations GRS Genetic risk score GWAS Genome-wide association studies HNF1B Hepatocyte nuclear factor 1 homeobox B IGF-1 Insulin-like growth factor 1 JAZF1 JAZF zinc finger 1 KLF14 Kruppel-like factor 14 MR Mendelian randomisation PPARγ Peroxisome proliferator-activated receptor γ RCT Randomised controlled trial TCF7L2 Transcription factor 7 like 2

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Genome-Wide and Abdominal MRI Data Provide Evidence That a Genetically Determined Favorable Adiposity Phenotype Is Characterized by Lower Ectopic Liver Fat and Lower Risk of Type 2 Diabetes, Heart Disease, and Hypertension

Cited by 79 publications

References 65 publications

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

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

Sub‐phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets

Using genetics to decipher the link between type 2 diabetes and cancer: shared aetiology or downstream consequence?

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