| 1. |
- Wilman, H. R., et al.
(author)
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Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration
- 2019
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In: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 71:3, s. 594-602
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Journal article (peer-reviewed)abstract
- Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10−8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart disease. We identified 3 genetic variants that are linked to an increased risk of developing higher liver iron content. We show that the same genetic variants are linked to higher risk of many diseases, but they may also be associated with some health advantages. Finally, we use genetic variants associated with waist-to-hip ratio as a tool to show that central obesity is causally associated with increased liver iron content.
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| 2. |
- Agudelo, Leandro Z., et al.
(author)
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Metabolic resilience is encoded in genome plasticity
- 2021
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Other publication (other academic/artistic)abstract
- Metabolism plays a central role in evolution, as resource conservation is a selective pressure for fitness and survival.Resource-driven adaptations offer a good model to study evolutionary innovation more broadly. It remains unknown howresource-driven optimization of genome function integrates chromatin architecture with transcriptional phase transitions.Here we show that tuning of genome architecture and heterotypic transcriptional condensates mediate resilience tonutrient limitation. Network genomic integration of phenotypic, structural, and functional relationships reveals that fattissue promotes organismal adaptations through metabolic acceleration chromatin domains and heterotypic PGC1Acondensates. We find evolutionary adaptations in several dimensions; low conservation of amino acid residues withinprotein disorder regions, nonrandom chromatin location of metabolic acceleration domains, condensate-chromatin stabilitythrough cis-regulatory anchoring and encoding of genome plasticity in radial chromatin organization. We show thatenvironmental tuning of these adaptations leads to fasting endurance, through efficient nuclear compartmentalization oflipid metabolic regions, and, locally, human-specific burst kinetics of lipid cycling genes. This process reduces oxidativestress, and fatty-acid mediated cellular acidification, enabling endurance of condensate chromatin conformations.Comparative genomics of genetic and diet perturbations reveal mammalian convergence of phenotype and structuralrelationships, along with loss of transcriptional control by diet-induced obesity. Further, we find that radial transcriptionalorganization is encoded in functional divergence of metabolic disease variant-hubs, heterotypic condensate composition,and protein residues sensing metabolic variation. During fuel restriction, these features license the formation of largeheterotypic condensates that buffer proton excess, and shift viscoelasticity for condensate endurance. This mechanismmaintains physiological pH, reduces pH-resilient inflammatory gene programs, and enables genome plasticity throughtranscriptionally driven cell-specific chromatin contacts. In vivo manipulation of this circuit promotes fasting-likeadaptations with heterotypic nuclear compartments, metabolic and cell-specific homeostasis. In sum, we uncover here ageneral principle by which transcription uses environmental fluctuations for genome function, and demonstrate howresource conservation optimizes transcriptional self-organization through robust feedback integrators, highlighting obesityas an inhibitor of genome plasticity relevant for many diseases.
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| 3. |
- Atabaki Pasdar, Naeimeh
(author)
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Enhancing prediction and causal inference in metabolic dyshomeostasis
- 2020
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Doctoral thesis (other academic/artistic)abstract
- This thesis is focused on two globally prevalent diseases: i) non-alcoholic fatty liver disease (NAFLD) and ii) type 2 diabetes (T2D), with an overall aim of improving prediction and causal inference in the context of these conditions. Our projects were mainly conducted using IMI DIRECT and UK Biobank datasets including multi-omics data, extensive environmental exposures, and biological intermediates.In paper I, we utilized structural equation modeling to test the 'twin-cycle' hypothesis concerning interactions between the liver and the pancreas in the etiology of T2D. Furthermore, the association of physical activity with glycemic control was investigated within the twin-cycle hypothesis. Our results showed the association of physical activity with several metabolic traits and factors. Moreover, the mediation effect of basal insulin secretion rate, insulin sensitivity and liver fat was identified from physical activity towards glucose regulation.In paper II, we developed a series of machine learning-based models for the diagnosis of fatty liver, using different combinations of complex clinical and omics input data, to screen at-risk populations for NAFLD. Beta-cell function and insulin sensitivity appeared to be the most informative predictors in the developed diagnostic models. Furthermore, the derived importance lists of each data set (clinical, genetic, transcriptomic, proteomic, and metabolomic) were highlighting previous findings and suggesting potential molecular features of the NAFLD etiology.In paper III, Bayesian network and Mendelian randomization approaches were deployed to examine a range of putative causal associations underlying the development of fatty liver. Our analyses identified basal insulin secretion rate and visceral fat as two key drivers. In addition, the sensitivity analysis on diabetes and non-diabetes strata identified a network mostly dominated by dysglycemia in presence of T2D, whereas, it was mainly controlled by excess adiposity in the absence of T2D. In paper IV, genotype-based recall (GBR) clinical trials, in which the genetic burden of individuals is used in recruiting two groups of participants with a high and low genetic risk score, were simulated and compared with the conventional randomized controlled trials (RCTs) in terms of their statistical power and the required sample sizes. The analysis showed that GBR trials are, under several diverse scenarios, more powerful than conventional RCTs for testing gene-treatment interactions.
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| 4. |
- Atabaki-Pasdar, Naeimeh, et al.
(author)
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Inferring causal pathways between metabolic processes and liver fat accumulation: an IMI DIRECT study
- 2021
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Other publication (other academic/artistic)abstract
- Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) often co-occur. Defining causal pathways underlying this relationship may help optimize the prevention and treatment of both diseases. Thus, we assessed the strength and magnitude of the putative causal pathways linking dysglycemia and fatty liver, using a combination of causal inference methods.Measures of glycemia, insulin dynamics, magnetic resonance imaging (MRI)-derived abdominal and liver fat content, serological biomarkers, lifestyle, and anthropometry were obtained in participants from the IMI DIRECT cohorts (n=795 with new onset T2D and 2234 individuals free from diabetes). UK Biobank (n=3641) was used for modelling and replication purposes. Bayesian networks were employed to infer causal pathways, with causal validation using two-sample Mendelian randomization.Bayesian networks fitted to IMI DIRECT data identified higher basal insulin secretion rate (BasalISR) and MRI-derived excess visceral fat (VAT) accumulation as the features of dysmetabolism most likely to cause liver fat accumulation; the unconditional probability of fatty liver (>5%) increased significantly when conditioning on high levels of BasalISR and VAT (by 23%, 32% respectively; 40% for both). Analyses in UK Biobank yielded comparable results. MR confirmed most causal pathways predicted by the Bayesian networks.Here, BasalISR had the highest causal effect on fatty liver predisposition, providing mechanistic evidence underpinning the established association of NAFLD and T2D. BasalISR may represent a pragmatic biomarker for NAFLD prediction in clinical practice.Competing Interest StatementHR is an employee and shareholder of Sanofi. MIM: The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. MIM has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, MIM is an employee of Genentech, and a holder of Roche stock. AM is a consultant for Lilly and has received research grants from several diabetes drug companies. PWF has received research grants from numerous diabetes drug companies and fess as consultant from Novo Nordisk, Lilly, and Zoe Global Ltd. He is currently the Scientific Director in Patient Care at the Novo Nordisk Foundation. Other authors declare non competing interests.Funding StatementThe work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement 115317 (DIRECT) resources of which are composed of financial contribution from the European Union Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. NAP is supported in part by Henning och Johan Throne-Holsts Foundation, Hans Werthen Foundation, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. HPM is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. AGJ is supported by an NIHR Clinician Scientist award (17/0005624). RK is funded by the Novo Nordisk Foundation (NNF18OC0031650) as part of a postdoctoral fellowship, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. AK, PM, HF, JF and GNG are supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. TJM is funded by an NIHR clinical senior lecturer fellowship. S.Bru acknowledges support from the Novo Nordisk Foundation (grants NNF17OC0027594 and NNF14CC0001). ATH is a Wellcome Trust Senior Investigator and is also supported by the NIHR Exeter Clinical Research Facility. JMS acknowledges support from Science for Life Laboratory (Plasma Profiling Facility), Knut and Alice Wallenberg Foundation (Human Protein Atlas) and Erling-Persson Foundation (KTH Centre for Precision Medicine). MIM is supported by the following grants; Wellcome (090532, 098381, 106130, 203141, 212259); NIH (U01-DK105535). PWF is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Approval for the study protocol was obtained from each of the regional research ethics review boards separately (Lund, Sweden: 20130312105459927, Copenhagen, Denmark: H-1-2012-166 and H-1-2012-100, Amsterdam, Netherlands: NL40099.029.12, Newcastle, Dundee and Exeter, UK: 12/NE/0132), and all participants provided written informed consent at enrolment. The research conformed to the ethical principles for medical research involving human participants outlined in the Declaration of Helsinki.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAuthors agree to make data and materials supporting the results or analyses presented in their paper available upon reasonable request
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| 5. |
- Atabaki Pasdar, Naeimeh, et al.
(author)
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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
- 2020
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In: PLoS Medicine. - San Francisco : Public Library of Science (PLoS). - 1549-1676 .- 1549-1277. ; 17:6, s. 1003149-1003149
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Journal article (peer-reviewed)abstract
- BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (
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| 6. |
- Atabaki-Pasdar, Naeimeh, et al.
(author)
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Statistical power considerations in genotype-based recall randomized controlled trials
- 2016
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
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Journal article (peer-reviewed)abstract
- Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.
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| 7. |
- Coral, Daniel E, et al.
(author)
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A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes
- 2023
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In: Nature Metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 5:2, s. 237-247
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Journal article (peer-reviewed)abstract
- Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.
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| 8. |
- Cresswell, Emily, et al.
(author)
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The value of neck adipose tissue as a predictor for metabolic risk in health and type 2 diabetes
- 2024
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In: Biochemical Pharmacology. - 0006-2952. ; 223
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Journal article (peer-reviewed)abstract
- Upper-body adiposity is adversely associated with metabolic health whereas the opposite is observed for the lower-body. The neck is a unique upper-body fat depot in adult humans, housing thermogenic brown adipose tissue (BAT), which is increasingly recognised to influence whole-body metabolic health. Loss of BAT, concurrent with replacement by white adipose tissue (WAT), may contribute to metabolic disease, and specific accumulation of neck fat is seen in certain conditions accompanied by adverse metabolic consequences. Yet, few studies have investigated the relationships between neck fat mass (NFM) and cardiometabolic risk, and the influence of sex and metabolic status. Typically, neck circumference (NC) is used as a proxy for neck fat, without considering other determinants of NC, including variability in neck lean mass. In this study we develop and validate novel methods to quantify NFM using dual x-ray absorptiometry (DEXA) imaging, and subsequently investigate the associations of NFM with metabolic biomarkers across approximately 7000 subjects from the Oxford BioBank. NFM correlated with systemic insulin resistance (Homeostatic Model Assessment for Insulin Resistance; HOMA-IR), low-grade inflammation (plasma high-sensitivity C-Reactive Protein; hsCRP), and metabolic markers of adipose tissue function (plasma triglycerides and non-esterified fatty acids; NEFA). NFM was higher in men than women, higher in type 2 diabetes mellitus compared with non-diabetes, after adjustment for total body fat, and also associated with overall cardiovascular disease risk (calculated QRISK3 score). This study describes the development of methods for accurate determination of NFM at scale and suggests a specific relationship between NFM and adverse metabolic health.
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| 9. |
- Ji, Yingjie, et al.
(author)
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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
- 2019
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In: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 68:1, s. 207-219
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Journal article (peer-reviewed)abstract
- Recent genetic studies have identified alleles associated with opposite effects on adiposity and risk of type 2 diabetes. We aimed to identify more of these variants and test the hypothesis that such favorable adiposity alleles are associated with higher subcutaneous fat and lower ectopic fat. We combined MRI data with genome-wide association studies of body fat percentage (%) and metabolic traits. We report 14 alleles, including 7 newly characterized alleles, associated with higher adiposity but a favorable metabolic profile. Consistent with previous studies, individuals carrying more favorable adiposity alleles had higher body fat % and higher BMI but lower risk of type 2 diabetes, heart disease, and hypertension. These individuals also had higher subcutaneous fat but lower liver fat and a lower visceral-to-subcutaneous adipose tissue ratio. Individual alleles associated with higher body fat % but lower liver fat and lower risk of type 2 diabetes included those in PPARG, GRB14, and IRS1, whereas the allele in ANKRD55 was paradoxically associated with higher visceral fat but lower risk of type 2 diabetes. Most identified favorable adiposity alleles are associated with higher subcutaneous and lower liver fat, a mechanism consistent with the beneficial effects of storing excess triglycerides in metabolically low-risk depots.
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| 10. |
- Jujić, Amra, et al.
(author)
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Glucose-dependent insulinotropic peptide and risk of cardiovascular events and mortality : a prospective study
- 2020
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In: Diabetologia. - : Springer. - 0012-186X .- 1432-0428. ; 63:5, s. 1043-1054
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Journal article (peer-reviewed)abstract
- Aims/hypothesis: Evidence that glucose-dependent insulinotropic peptide (GIP) and/or the GIP receptor (GIPR) are involved in cardiovascular biology is emerging. We hypothesised that GIP has untoward effects on cardiovascular biology, in contrast to glucagon-like peptide 1 (GLP-1), and therefore investigated the effects of GIP and GLP-1 concentrations on cardiovascular disease (CVD) and mortality risk.Methods: GIP concentrations were successfully measured during OGTTs in two independent populations (Malmo Diet Cancer-Cardiovascular Cohort [MDC-CC] and Prevalence, Prediction and Prevention of Diabetes in Botnia [PPP-Botnia]) in a total of 8044 subjects. GLP-1 (n = 3625) was measured in MDC-CC. The incidence of CVD and mortality was assessed via national/regional registers or questionnaires. Further, a two-sample Mendelian randomisation (2SMR) analysis between the GIP pathway and outcomes (coronary artery disease [CAD] and myocardial infarction) was carried out using a GIP-associated genetic variant, rs1800437, as instrumental variable. An additional reverse 2SMR was performed with CAD as exposure variable and GIP as outcome variable, with the instrumental variables constructed from 114 known genetic risk variants for CAD.Results: In meta-analyses, higher fasting levels of GIP were associated with risk of higher total mortality (HR[95% CI] = 1.22 [1.11, 1.35]; p = 4.5 x 10(-5)) and death from CVD (HR[95% CI] 1.30 [1.11, 1.52]; p = 0.001). In accordance, 2SMR analysis revealed that increasing GIP concentrations were associated with CAD and myocardial infarction, and an additional reverse 2SMR revealed no significant effect of CAD on GIP levels, thus confirming a possible effect solely of GIP on CAD.Conclusions/interpretation: In two prospective, community-based studies, elevated levels of GIP were associated with greater risk of all-cause and cardiovascular mortality within 5-9 years of follow-up, whereas GLP-1 levels were not associated with excess risk. Further studies are warranted to determine the cardiovascular effects of GIP per se.
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