Showing papers by "Martin Ridderstråle published in 2020"
Lund University1, Halmstad University2, University of Geneva3, Swiss Institute of Bioinformatics4, Aventis Pharma5, University of Exeter6, University of Westminster7, University of Oxford8, University of Dundee9, Public Health Research Institute10, University of Helsinki11, University of Copenhagen12, Royal Institute of Technology13, Technical University of Denmark14, Newcastle University15, University of Eastern Finland16, Lille University of Science and Technology17, Novo Nordisk18, University of Amsterdam19, Leiden University20, National University of Singapore21, Technische Universität München22, University of Cambridge23, Imperial College London24, University of Southern Denmark25, National Research Council26, Genentech27, Eli Lilly and Company28, Harvard University29
TL;DR: Several models with different combinations of clinical and omics data were developed and identified biological features that appear to be associated with liver fat accumulation and outperformed existing noninvasive NAFLD prediction tools.
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 (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.
51 citations
Technical University of Denmark1, University of Copenhagen2, Frederiksberg Hospital3, Utrecht University4, Public Health Research Institute5, University of Helsinki6, Copenhagen University Hospital7, University of Dundee8, Lund University9, Novo Nordisk10, University of Southern Denmark11, University of Exeter12, Newcastle University13, Royal Institute of Technology14, University of Oxford15, University of Eastern Finland16, National Research Council17, Eli Lilly and Company18, Leiden University Medical Center19, University of Geneva20, Swiss Institute of Bioinformatics21, Technische Universität München22, National University of Singapore23, John Radcliffe Hospital24
TL;DR: A holistic view of the regulation and molecular context of whole blood transcriptomic modules in the context of metabolic disease is provided and point to novel biological candidates for future studies related to T2D.
Abstract: The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.
8 citations
VU University Medical Center1, Utrecht University2, Leiden University Medical Center3, VU University Amsterdam4, Lund University5, University of Oxford6, University of Eastern Finland7, University of Copenhagen8, University of Southern Denmark9, Eli Lilly and Company10, University of Dundee11, National Research Council12, Wellcome Trust Centre for Human Genetics13, Newcastle University14, University of Exeter15, Technical University of Denmark16, Swiss Institute of Bioinformatics17, University of Geneva18, Royal Institute of Technology19, National University of Singapore20, Technische Universität München21, Harvard University22
TL;DR: Different glycaemia profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D.
Abstract: AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.
5 citations
Lund University1, Halmstad University2, Swiss Institute of Bioinformatics3, University of Geneva4, University of Exeter5, University of Westminster6, University of Oxford7, University of Dundee8, Public Health Research Institute9, University of Helsinki10, Copenhagen University Hospital11, Royal Institute of Technology12, Technical University of Denmark13, University of Copenhagen14, Frederiksberg Hospital15, Newcastle University16, University of Eastern Finland17, university of lille18, Steno Diabetes Center19, Leiden University Medical Center20, National University of Singapore21, Technische Universität München22, University of Cambridge23, Imperial College London24, University of Southern Denmark25, Utrecht University26, National Research Council27, Eli Lilly and Company28, Umeå University29, Harvard University30
TL;DR: Clinical useful liver fat prediction models are developed and biological features that appear to affect liver fat accumulation are identified and out-performed existing non-invasive NAFLD prediction tools.
Abstract: Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in type 2 diabetes (T2D) and beyond. Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and ultimately hepatocellular carcinomas. Methods and Findings Utilizing the baseline data from the IMI DIRECT participants (n=1514) we sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Multi-omic (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, and 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 ( Conclusions We have developed clinically useful liver fat prediction models (see: www.predictliverfat.org) and identified biological features that appear to affect liver fat accumulation.