Publications by authors named "Søren Brunak"

280 Publications

Profiles of glucose metabolism in different prediabetes phenotypes, classified by fasting glycemia, 2-hour OGTT, glycated hemoglobin, and 1-hour OGTT: An IMI DIRECT study.

Diabetes 2021 Jul 7. Epub 2021 Jul 7.

Department of Epidemiology and Data Science, Amsterdam Medical Centre, location VUMC, Amsterdam, the Netherlands.

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (=2111) underwent 2h-75g OGTT at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose, IFG; impaired glucose tolerance, IGT; HbA1c-prediabetes, IA1c), two defects (IFG+IGT, IFG+IA1c, IGT+IA1c), or all defects (IFG+IGT+IA1c). Beta-cell function (BCF) and insulin sensitivity (IS) were assessed from OGTT. At baseline, when pooling participants with isolated defects, they showed impairment in both BCF and IS compared to healthy controls. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, IGT showed lower IS, insulin secretion at reference glucose (ISR), and insulin secretion potentiation (p<0.002). Conversely, IA1c showed higher IS and ISR (p<0.0001). Among groups with two defects, we similarly found differences in both BCF and IS. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, p<0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared to the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.
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http://dx.doi.org/10.2337/db21-0227DOI Listing
July 2021

Acute and persistent symptoms in non-hospitalized PCR-confirmed COVID-19 patients.

Sci Rep 2021 06 23;11(1):13153. Epub 2021 Jun 23.

Department of Medical Endocrinology and Metabolism, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark.

Reports of persistent symptoms after hospitalization with COVID-19 have raised concern of a "long COVID" syndrome. This study aimed at determining the prevalence of and risk factors for acute and persistent symptoms in non-hospitalized patients with polymerase chain reaction (PCR) confirmed COVID-19. We conducted a cohort study of non-hospitalized participants identified via the Danish Civil Registration System with a SARS-CoV-2-positive PCR-test and available biobank samples. Participants received a digital questionnaire on demographics and COVID-19-related symptoms. Persistent symptoms: symptoms > 4 weeks (in sensitivity analyses > 12 weeks). We included 445 participants, of whom 34% were asymptomatic. Most common acute symptoms were fatigue, headache, and sneezing, while fatigue and reduced smell and taste were most severe. Persistent symptoms, most commonly fatigue and memory and concentration difficulties, were reported by 36% of 198 symptomatic participants with follow-up > 4 weeks. Risk factors for persistent symptoms included female sex (women 44% vs. men 24%, odds ratio 2.7, 95% CI 1.4-5.1, p = 0.003) and BMI (odds ratio 1.1, 95% CI 1.0-1.2, p = 0.001). In conclusion, among non-hospitalized PCR-confirmed COVID-19 patients one third were asymptomatic while one third of symptomatic participants had persistent symptoms illustrating the heterogeneity of disease presentation. These findings should be considered in health care planning and policy making related to COVID-19.
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http://dx.doi.org/10.1038/s41598-021-92045-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8222239PMC
June 2021

Impaired Vitamin D Signaling in T Cells From a Family With Hereditary Vitamin D Resistant Rickets.

Front Immunol 2021 19;12:684015. Epub 2021 May 19.

The LEO Foundation Skin Immunology Research Center, Department of Immunology and Microbiology, University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark.

The active form of vitamin D, 1,25-dihydroxyvitamin D (1,25(OH)D), mediates its immunomodulatory effects by binding to the vitamin D receptor (VDR). Here, we describe a new point mutation in the DNA-binding domain of the VDR and its consequences for 1,25(OH)D signaling in T cells from heterozygous and homozygous carriers of the mutation. The mutation did not affect the overall structure or the ability of the VDR to bind 1,25(OH)D and the retinoid X receptor. However, the subcellular localization of the VDR was strongly affected and the transcriptional activity was abolished by the mutation. In heterozygous carriers of the mutation, 1,25(OH)D-induced gene regulation was reduced by ~ 50% indicating that the expression level of wild-type VDR determines 1,25(OH)D responsiveness in T cells. We show that vitamin D-mediated suppression of vitamin A-induced gene regulation depends on an intact ability of the VDR to bind DNA. Furthermore, we demonstrate that vitamin A inhibits 1,25(OH)D-induced translocation of the VDR to the nucleus and 1,25(OH)D-induced up-regulation of CYP24A1. Taken together, this study unravels novel aspects of vitamin D signaling and function of the VDR in human T cells.
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http://dx.doi.org/10.3389/fimmu.2021.684015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170129PMC
May 2021

Eleven genomic loci affect plasma levels of chronic inflammation marker soluble urokinase-type plasminogen activator receptor.

Commun Biol 2021 Jun 2;4(1):655. Epub 2021 Jun 2.

Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark.

Soluble urokinase-type plasminogen activator receptor (suPAR) is a chronic inflammation marker associated with the development of a range of diseases, including cancer and cardiovascular disease. The genetics of suPAR remain unexplored but may shed light on the biology of the marker and its connection to outcomes. We report a heritability estimate of 60% for the variation in suPAR and performed a genome-wide association meta-analysis on suPAR levels measured in Iceland (N = 35,559) and in Denmark (N = 12,177). We identified 13 independently genome-wide significant sequence variants associated with suPAR across 11 distinct loci. Associated variants were found in and around genes encoding uPAR (PLAUR), its ligand uPA (PLAU), the kidney-disease-associated gene PLA2R1 as well as genes with relations to glycosylation, glycoprotein biosynthesis, and the immune response. These findings provide new insight into the causes of variation in suPAR plasma levels, which may clarify suPAR's potential role in associated diseases, as well as the underlying mechanisms that give suPAR its prognostic value as a unique marker of chronic inflammation.
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http://dx.doi.org/10.1038/s42003-021-02144-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172928PMC
June 2021

Endotrophin is associated with chronic multimorbidity and all-cause mortality in a cohort of elderly women.

EBioMedicine 2021 Jun 24;68:103391. Epub 2021 May 24.

Nordic Bioscience, DK-2730 Herlev, Denmark.

Background: The signalling peptide endotrophin is derived through proteolytic cleavage of the carboxyl-terminal during formation of type VI collagen. It is expressed by most descendants of the mesenchymal stem cells lineage, including adipocytes and fibroblasts, and have been proposed to be a central extracellular matrix hormone associated with several age-related diseases. We aimed to assess the association of endotrophin with chronic disease incidence and death in older women.

Methods: 5,602 elderly Danish women from the observational, prospective cohort: The Prospective Epidemiological Risk Factor (PERF) study were included in the analysis which covered baseline (BL) and follow-up (FU) 14 years later. An elastic net was used to investigate the relative importance of 58 variables to serum endotrophin-levels. 20 chronic diseases were defined on the basis of clinical variables available along with diagnoses extracted from both the National Patient Register, the National Diabetes Register and the Danish Cancer Registry. The cross-sectional associations between endotrophin-levels and these 17 chronic age-related diseases were investigated using logistic regression and a set-analysis explored disease-combinations within multimorbidity. The association of endotrophin with mortality was assessed by Cox proportional hazard models.

Findings: Formation of type III collagen (PRO-C3), age and creatine-levels were the most influential variables of endotrophin-levels. Several chronic diseases were significantly associated with endotrophin-levels independent of age and BMI including chronic kidney disease (BL OR=3.7, p < 0.001; FU OR = 7.9 p < 0.001), diabetes (BL OR = 1.5, p = 0.0015, FU OR=1.6, p = 0.004) and peripheral arterial disease (BL OR = 1.3, p = 0.029; FU OR=2.4, p < 0.001). Lastly, endotrophin-levels were significantly rising with number of morbidities (p < 0.001) and a predictor of death after adjusting for age and BMI (BL HR=1.95; FU HR = 2.00).

Interpretation: Endotrophin was associated with death and increased with number of morbidities. Endotrophin may be a central hormone of fibroblast that warrant investigation and possible targeted intervention in several chronic diseases.

Funding: The funder of the PERF study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
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http://dx.doi.org/10.1016/j.ebiom.2021.103391DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8167215PMC
June 2021

Understanding inherent image features in CNN-based assessment of diabetic retinopathy.

Sci Rep 2021 May 6;11(1):9704. Epub 2021 May 6.

Australian E-Health Research Centre, CSIRO, Perth, Australia.

Diabetic retinopathy (DR) is a leading cause of blindness and affects millions of people throughout the world. Early detection and timely checkups are key to reduce the risk of blindness. Automated grading of DR is a cost-effective way to ensure early detection and timely checkups. Deep learning or more specifically convolutional neural network (CNN)-based methods produce state-of-the-art performance in DR detection. Whilst CNN based methods have been proposed, no comparisons have been done between the extracted image features and their clinical relevance. Here we first adopt a CNN visualization strategy to discover the inherent image features involved in the CNN's decision-making process. Then, we critically analyze those features with respect to commonly known pathologies namely microaneurysms, hemorrhages and exudates, and other ocular components. We also critically analyze different CNNs by considering what image features they pick up during learning to predict and justify their clinical relevance. The experiments are executed on publicly available fundus datasets (EyePACS and DIARETDB1) achieving an accuracy of 89 ~ 95% with AUC, sensitivity and specificity of respectively 95 ~ 98%, 74 ~ 86%, and 93 ~ 97%, for disease level grading of DR. Whilst different CNNs produce consistent classification results, the rate of picked-up image features disagreement between models could be as high as 70%.
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http://dx.doi.org/10.1038/s41598-021-89225-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8102512PMC
May 2021

Detection of Molecular Signatures of Homologous Recombination Deficiency in Bladder Cancer.

Clin Cancer Res 2021 Jul 4;27(13):3734-3743. Epub 2021 May 4.

Danish Cancer Society Research Center, Copenhagen, Denmark.

Purpose: Poly (ADP ribose)-polymerase (PARP) inhibitors are approved for use in breast, ovarian, prostate, and pancreatic cancers, which are the solid tumor types that most frequently have alterations in key homologous recombination (HR) genes, such as . However, the frequency of HR deficiency (HRD) in other solid tumor types, including bladder cancer, is less well characterized.

Experimental Design: Specific DNA aberration profiles (mutational signatures) are induced by HRD, and the presence of these "genomic scars" can be used to assess the presence or absence of HRD in a given tumor biopsy even in the absence of an observed alteration of an HR gene. Using whole-exome and whole-genome data, we measured various HRD-associated mutational signatures in bladder cancer.

Results: We found that a subset of bladder tumors have evidence of HRD. In addition to a small number of tumors with biallelic events, approximately 10% of bladder tumors had significant evidence of HRD-associated mutational signatures. Increased levels of HRD signatures were associated with promoter methylation of , which encodes CtIP, a key protein involved in HR.

Conclusions: A subset of bladder tumors have genomic features suggestive of HRD and therefore may be more likely to benefit from therapies such as platinum agents and PARP inhibitors that target tumor HRD.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-5037DOI Listing
July 2021

Genome-wide analysis of 944 133 individuals provides insights into the etiology of haemorrhoidal disease.

Gut 2021 Apr 22. Epub 2021 Apr 22.

Department of Medicine I, Institute of Cancer Research, Medical University Vienna, Vienna, Austria.

Objective: Haemorrhoidal disease (HEM) affects a large and silently suffering fraction of the population but its aetiology, including suspected genetic predisposition, is poorly understood. We report the first genome-wide association study (GWAS) meta-analysis to identify genetic risk factors for HEM to date.

Design: We conducted a GWAS meta-analysis of 218 920 patients with HEM and 725 213 controls of European ancestry. Using GWAS summary statistics, we performed multiple genetic correlation analyses between HEM and other traits as well as calculated HEM polygenic risk scores (PRS) and evaluated their translational potential in independent datasets. Using functional annotation of GWAS results, we identified HEM candidate genes, which differential expression and coexpression in HEM tissues were evaluated employing RNA-seq analyses. The localisation of expressed proteins at selected loci was investigated by immunohistochemistry.

Results: We demonstrate modest heritability and genetic correlation of HEM with several other diseases from the GI, neuroaffective and cardiovascular domains. HEM PRS validated in 180 435 individuals from independent datasets allowed the identification of those at risk and correlated with younger age of onset and recurrent surgery. We identified 102 independent HEM risk loci harbouring genes whose expression is enriched in blood vessels and GI tissues, and in pathways associated with smooth muscles, epithelial and endothelial development and morphogenesis. Network transcriptomic analyses highlighted HEM gene coexpression modules that are relevant to the development and integrity of the musculoskeletal and epidermal systems, and the organisation of the extracellular matrix.

Conclusion: HEM has a genetic component that predisposes to smooth muscle, epithelial and connective tissue dysfunction.
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http://dx.doi.org/10.1136/gutjnl-2020-323868DOI Listing
April 2021

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.

Pancreas 2021 03;50(3):251-279

Sander Lab, Harvard Medical School, Boston, MA.

Abstract: Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.
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http://dx.doi.org/10.1097/MPA.0000000000001762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041569PMC
March 2021

Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program.

BMJ Open Diabetes Res Care 2021 03;9(1)

Biostatistics Center and Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Rockville, Maryland, USA

Introduction: Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes.

Research Design And Methods: Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility.

Results: Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study.

Conclusions: NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study.

Trial Registration Number: Diabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.
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http://dx.doi.org/10.1136/bmjdrc-2020-001953DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016090PMC
March 2021

The burden of disease of three food-associated heavy metals in clusters in the Danish population - Towards targeted public health strategies.

Food Chem Toxicol 2021 Apr 18;150:112072. Epub 2021 Feb 18.

Division of Diet, Disease Prevention and Toxicology, National Food Institute, Technical University of Denmark, Denmark. Electronic address:

Lifestyle and sociodemographics are likely to influence dietary patterns, and, as a result, human exposure to chemical contaminants in foods and their associated health impact. We aimed to characterize subgroups of the Danish population based on diet and sociodemographic indicators, and identify those bearing a higher disease burden due to exposure to methylmercury (MeHg), cadmium (Cd) and inorganic arsenic (i-As). We collected dietary, lifestyle, and sociodemographic data on the occurrence of chemical contaminants in foods from Danish surveys. We grouped participants according to similarities in diet, lifestyle, and sociodemographics using Self-Organizing Maps (SOM), and estimated disease burden in disability-adjusted life years (DALY). SOM clustering resulted in 12 population groups with distinct characteristics. Exposure to contaminants varied between clusters and was largely driven by intake of fish, seafood and cereal products. Five clusters had an estimated annual burden >20 DALY/100,000. The cluster with the highest burden had a high proportion of women of childbearing age, with most of the burden attributed to MeHg. Individuals belonging to the top three clusters had higher education and physical activity, were mainly non-smokers and lived in urban areas. Our findings may facilitate the development of preventive strategies targeted to the most affected subgroups.
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http://dx.doi.org/10.1016/j.fct.2021.112072DOI Listing
April 2021

Genetic insight into sick sinus syndrome.

Eur Heart J 2021 05;42(20):1959-1971

deCODE genetics/Amgen, Inc., Sturlugata 8, Reykjavik 101, Iceland.

Aims: The aim of this study was to use human genetics to investigate the pathogenesis of sick sinus syndrome (SSS) and the role of risk factors in its development.

Methods And Results: We performed a genome-wide association study of 6469 SSS cases and 1 000 187 controls from deCODE genetics, the Copenhagen Hospital Biobank, UK Biobank, and the HUNT study. Variants at six loci associated with SSS, a reported missense variant in MYH6, known atrial fibrillation (AF)/electrocardiogram variants at PITX2, ZFHX3, TTN/CCDC141, and SCN10A and a low-frequency (MAF = 1.1-1.8%) missense variant, p.Gly62Cys in KRT8 encoding the intermediate filament protein keratin 8. A full genotypic model best described the p.Gly62Cys association (P = 1.6 × 10-20), with an odds ratio (OR) of 1.44 for heterozygotes and a disproportionally large OR of 13.99 for homozygotes. All the SSS variants increased the risk of pacemaker implantation. Their association with AF varied and p.Gly62Cys was the only variant not associating with any other arrhythmia or cardiovascular disease. We tested 17 exposure phenotypes in polygenic score (PGS) and Mendelian randomization analyses. Only two associated with the risk of SSS in Mendelian randomization, AF, and lower heart rate, suggesting causality. Powerful PGS analyses provided convincing evidence against causal associations for body mass index, cholesterol, triglycerides, and type 2 diabetes (P > 0.05).

Conclusion: We report the associations of variants at six loci with SSS, including a missense variant in KRT8 that confers high risk in homozygotes and points to a mechanism specific to SSS development. Mendelian randomization supports a causal role for AF in the development of SSS.
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http://dx.doi.org/10.1093/eurheartj/ehaa1108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140484PMC
May 2021

A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis.

Commun Biol 2021 02 3;4(1):156. Epub 2021 Feb 3.

deCODE genetics/Amgen Inc., Reykjavik, Iceland.

Iron is essential for many biological functions and iron deficiency and overload have major health implications. We performed a meta-analysis of three genome-wide association studies from Iceland, the UK and Denmark of blood levels of ferritin (N = 246,139), total iron binding capacity (N = 135,430), iron (N = 163,511) and transferrin saturation (N = 131,471). We found 62 independent sequence variants associating with iron homeostasis parameters at 56 loci, including 46 novel loci. Variants at DUOX2, F5, SLC11A2 and TMPRSS6 associate with iron deficiency anemia, while variants at TF, HFE, TFR2 and TMPRSS6 associate with iron overload. A HBS1L-MYB intergenic region variant associates both with increased risk of iron overload and reduced risk of iron deficiency anemia. The DUOX2 missense variant is present in 14% of the population, associates with all iron homeostasis biomarkers, and increases the risk of iron deficiency anemia by 29%. The associations implicate proteins contributing to the main physiological processes involved in iron homeostasis: iron sensing and storage, inflammation, absorption of iron from the gut, iron recycling, erythropoiesis and bleeding/menstruation.
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http://dx.doi.org/10.1038/s42003-020-01575-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859200PMC
February 2021

Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis.

NPJ Digit Med 2021 Jan 29;4(1):12. Epub 2021 Jan 29.

Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

Diagnostic errors are common and can lead to harmful treatments. We present a data-driven, generic approach for identifying patients at risk of being mis- or overdiagnosed, here exemplified by chronic obstructive pulmonary disease (COPD). It has been estimated that 5-60% of all COPD cases are misdiagnosed. High-throughput methods are therefore needed in this domain. We have used a national patient registry, which contains hospital diagnoses for 6.9 million patients across the entire Danish population for 21 years and identified statistically significant disease trajectories for COPD patients. Using 284,154 patients diagnosed with COPD, we identified frequent disease trajectories comprising time-ordered comorbidities. Interestingly, as many as 42,459 patients did not present with these time-ordered, common comorbidities. Comparison of the individual disease history for each non-follower to the COPD trajectories, demonstrated that 9597 patients were unusual. Survival analysis showed that this group died significantly earlier than COPD patients following a trajectory. Out of the 9597 patients, we identified one subgroup comprising 2185 patients at risk of misdiagnosed COPD without the typical events of COPD patients. In all, 10% of these patients were diagnosed with lung cancer, and it seems likely that they are underdiagnosed for lung cancer as their laboratory test values and survival pattern are similar to such patients. Furthermore, only 4% had a lung function test to confirm the COPD diagnosis. Another subgroup with 2368 patients were found to be at risk of "classically" overdiagnosed COPD that survive >5.5 years after the COPD diagnosis, but without the typical complications of COPD.
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http://dx.doi.org/10.1038/s41746-021-00382-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846731PMC
January 2021

Chance of live birth: a nationwide, registry-based cohort study.

Hum Reprod 2021 03;36(4):1065-1073

Recurrent Pregnancy Loss Unit, Capital Region, Copenhagen University Hospital, Rigshospitalet, Fertility Clinic 4071, 2100 Copenhagen Ø, and Hvidovre Hospital, 2650 Hvidovre, Denmark.

Study Question: Does the sequence of prior pregnancy events (pregnancy losses, live births, ectopic pregnancies, molar pregnancy and still birth), obstetric complications and maternal age affect chance of live birth in the next pregnancy and are prior events predictive for the outcome?

Summary Answer: The sequence of pregnancy outcomes is significantly associated with chance of live birth; however, pregnancy history and age are insufficient to predict the outcome of an individual woman's next pregnancy.

What Is Known Already: Adverse pregnancy outcomes decrease the chance of live birth in the next pregnancy, whereas the impact of prior live births is less clear.

Study Design, Size, Duration: Nationwide, registry-based cohort study of 1 285 230 women with a total of 2 722 441 pregnancies from 1977 to 2017.

Participants/materials, Setting, Methods: All women living in Denmark in the study period with at least one pregnancy in either the Danish Medical Birth Registry or the Danish National Patient Registry. Data were analysed using logistic regression with a robust covariance model to account for women with more than one pregnancy. Model discrimination and calibration were ascertained using 20% of the women in the cohort randomly selected as an internal validation set.

Main Results And The Role Of Chance: Obstetric complications, still birth, ectopic pregnancies and pregnancy losses had a negative effect on the chance of live birth in the next pregnancy. Consecutive, identical pregnancy outcomes (pregnancy losses, live births or ectopic pregnancies) immediately preceding the next pregnancy had a larger impact than the total number of any outcome. Model discrimination was modest (C-index = 0.60, positive predictive value = 0.45), but the models were well calibrated.

Limitations, Reasons For Caution: While prior pregnancy outcomes and their sequence significantly influenced the chance of live birth, the discriminative abilities of the predictive models demonstrate clearly that pregnancy history and maternal age are insufficient to reliably predict the outcome of a given pregnancy.

Wider Implications Of The Findings: Prior pregnancy history has a significant impact on the chance of live birth in the next pregnancy. However, the results emphasize that only taking age and number of losses into account does not predict if a pregnancy will end as a live birth or not. A better understanding of biological determinants for pregnancy outcomes is urgently needed.

Study Funding/competing Interest(s): The work was supported by the Novo Nordisk Foundation, Ole Kirk Foundation and Rigshospitalet's Research Foundation. The authors have no financial relationships that could appear to have influenced the work.

Trial Registration Number: N/A.
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http://dx.doi.org/10.1093/humrep/deaa326DOI Listing
March 2021

ARDD 2020: from aging mechanisms to interventions.

Aging (Albany NY) 2020 12 30;12(24):24484-24503. Epub 2020 Dec 30.

Blavatnik Institute, Department of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 94107, USA.

Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence and advanced screening techniques, as well as a strong influence from the industry sector may lead to novel discoveries to treat age-related diseases. The present review summarizes presentations from the 7 Annual Aging Research and Drug Discovery (ARDD) meeting, held online on the 1 to 4 of September 2020. The meeting covered topics related to new methodologies to study aging, knowledge about basic mechanisms of longevity, latest interventional strategies to target the aging process as well as discussions about the impact of aging research on society and economy. More than 2000 participants and 65 speakers joined the meeting and we already look forward to an even larger meeting next year. Please mark your calendars for the 8 ARDD meeting that is scheduled for the 31 of August to 3 of September, 2021, at Columbia University, USA.
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http://dx.doi.org/10.18632/aging.202454DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803558PMC
December 2020

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.

Lancet Digit Health 2020 04 12;2(4):e179-e191. Epub 2020 Mar 12.

Department of Intensive Care, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.

Background: Many mortality prediction models have been developed for patients in intensive care units (ICUs); most are based on data available at ICU admission. We investigated whether machine learning methods using analyses of time-series data improved mortality prognostication for patients in the ICU by providing real-time predictions of 90-day mortality. In addition, we examined to what extent such a dynamic model could be made interpretable by quantifying and visualising the features that drive the predictions at different timepoints.

Methods: Based on the Simplified Acute Physiology Score (SAPS) III variables, we trained a machine learning model on longitudinal data from patients admitted to four ICUs in the Capital Region, Denmark, between 2011 and 2016. We included all patients older than 16 years of age, with an ICU stay lasting more than 1 h, and who had a Danish civil registration number to enable 90-day follow-up. We leveraged static data and physiological time-series data from electronic health records and the Danish National Patient Registry. A recurrent neural network was trained with a temporal resolution of 1 h. The model was internally validated using the holdout method with 20% of the training dataset and externally validated using previously unseen data from a fifth hospital in Denmark. Its performance was assessed with the Matthews correlation coefficient (MCC) and area under the receiver operating characteristic curve (AUROC) as metrics, using bootstrapping with 1000 samples with replacement to construct 95% CIs. A Shapley additive explanations algorithm was applied to the prediction model to obtain explanations of the features that drive patient-specific predictions, and the contributions of each of the 44 features in the model were analysed and compared with the variables in the original SAPS III model.

Findings: From a dataset containing 15 615 ICU admissions of 12 616 patients, we included 14 190 admissions of 11 492 patients in our analysis. Overall, 90-day mortality was 33·1% (3802 patients). The deep learning model showed a predictive performance on the holdout testing dataset that improved over the timecourse of an ICU stay: MCC 0·29 (95% CI 0·25-0·33) and AUROC 0·73 (0·71-0·74) at admission, 0·43 (0·40-0·47) and 0·82 (0·80-0·84) after 24 h, 0·50 (0·46-0·53) and 0·85 (0·84-0·87) after 72 h, and 0·57 (0·54-0·60) and 0·88 (0·87-0·89) at the time of discharge. The model exhibited good calibration properties. These results were validated in an external validation cohort of 5827 patients with 6748 admissions: MCC 0·29 (95% CI 0·27-0·32) and AUROC 0·75 (0·73-0·76) at admission, 0·41 (0·39-0·44) and 0·80 (0·79-0·81) after 24 h, 0·46 (0·43-0·48) and 0·82 (0·81-0·83) after 72 h, and 0·47 (0·44-0·49) and 0·83 (0·82-0·84) at the time of discharge.

Interpretation: The prediction of 90-day mortality improved with 1-h sampling intervals during the ICU stay. The dynamic risk prediction can also be explained for an individual patient, visualising the features contributing to the prediction at any point in time. This explanation allows the clinician to determine whether there are elements in the current patient state and care that are potentially actionable, thus making the model suitable for further validation as a clinical tool.

Funding: Novo Nordisk Foundation and the Innovation Fund Denmark.
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http://dx.doi.org/10.1016/S2589-7500(20)30018-2DOI Listing
April 2020

Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study.

Diabetes Care 2021 Feb 15;44(2):511-518. Epub 2020 Dec 15.

Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.

Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).

Research Design And Methods: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.

Results: Faster HbA progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles ( = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role.

Conclusions: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression.
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http://dx.doi.org/10.2337/dc20-1567DOI Listing
February 2021

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study.

Genome Med 2020 12 1;12(1):109. Epub 2020 Dec 1.

NIHR Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.
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http://dx.doi.org/10.1186/s13073-020-00806-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708171PMC
December 2020

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study.

PLoS One 2020 30;15(11):e0242360. Epub 2020 Nov 30.

Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242360PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703960PMC
January 2021

Identification of a Synthetic Lethal Relationship between Nucleotide Excision Repair Deficiency and Irofulven Sensitivity in Urothelial Cancer.

Clin Cancer Res 2021 Apr 18;27(7):2011-2022. Epub 2020 Nov 18.

Danish Cancer Society Research Center, Copenhagen, Denmark.

Purpose: Cisplatin-based chemotherapy is a first-line treatment for muscle-invasive and metastatic urothelial cancer. Approximately 10% of bladder urothelial tumors have a somatic missense mutation in the nucleotide excision repair (NER) gene, , which confers increased sensitivity to cisplatin-based chemotherapy. However, a significant subset of patients is ineligible to receive cisplatin-based therapy due to medical contraindications, and no NER-targeted approaches are available for platinum-ineligible or platinum-refractory -mutant cases.

Experimental Design: We used a series of NER-proficient and NER-deficient preclinical tumor models to test sensitivity to irofulven, an abandoned anticancer agent. In addition, we used available clinical and sequencing data from multiple urothelial tumor cohorts to develop and validate a composite mutational signature of deficiency and cisplatin sensitivity.

Results: We identified a novel synthetic lethal relationship between tumor NER deficiency and sensitivity to irofulven. Irofulven specifically targets cells with inactivation of the transcription-coupled NER (TC-NER) pathway and leads to robust responses and , including in models with acquired cisplatin resistance, while having minimal effect on cells with intact NER. We also found that a composite mutational signature of deficiency was strongly associated with cisplatin response in patients and was also associated with cisplatin and irofulven sensitivity in preclinical models.

Conclusions: Tumor NER deficiency confers sensitivity to irofulven, a previously abandoned anticancer agent, with minimal activity in NER-proficient cells. A composite mutational signature of NER deficiency may be useful in identifying patients likely to respond to NER-targeting agents, including cisplatin and irofulven..
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http://dx.doi.org/10.1158/1078-0432.CCR-20-3316DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026514PMC
April 2021

Semen quality and waiting time to pregnancy explored using association mining.

Andrology 2021 03 14;9(2):577-587. Epub 2020 Nov 14.

University Department of Growth and Reproduction, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.

Background: Assessment of semen quality is a key pillar in the evaluation of men from infertile couples. Usually, semen parameters are interpreted individually because the interactions between parameters are difficult to account for.

Objectives: To determine how combinations of classical semen parameters and female partner age were associated with waiting time to pregnancy (TTP).

Materials And Methods: Semen results of 500 fertile men, information of TTP, and partner age were used for regressions and to detect breaking points. For a modified Association Rule Mining algorithm, semen parameters were categorized as High, Medium, and Low.

Results: Men ≤32.1 years and women ≤32.9 years had shorter TTP than older. Decreasing TTP was associated with increasing level of individual semen parameters up to threshold values: sperm concentration 46 mill/mL, total sperm count 179 mill, progressive motility 63%, and normal morphology 11.5%. Using association mining, approximately 100 combinations of semen parameters and partner age were associated with TTP. TTP ≤ 1 month often co-occurred with high percentages of progressive motility (≥62%) and morphologically normal spermatozoa (≥10.5%). Furthermore, TTP ≤ 1 did not tend to appear with lower percentages of these two semen parameters or high partner age (≥32 years). However, high percentages of motile or normal spermatozoa could not compensate for sperm concentration ≤42 mill/mL or total sperm count ≤158 mill. The prolonging effect of high partner age could not be compensated for by the man's semen quality.

Discussion And Conclusion: Using association mining, we observed that TTP was best predicted when combinations of semen parameters were accounted for. Sperm counts, motility, and morphology were all important, and no single semen parameter was inferior. Additionally, female age above 32 years had a negative impact on TTP that could not be compensated for by high semen parameters of the man.
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http://dx.doi.org/10.1111/andr.12924DOI Listing
March 2021

Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients.

Nat Commun 2020 10 2;11(1):4952. Epub 2020 Oct 2.

Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200, Copenhagen, Denmark.

We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV).
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http://dx.doi.org/10.1038/s41467-020-18682-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532164PMC
October 2020

Alcoholic liver disease: A registry view on comorbidities and disease prediction.

PLoS Comput Biol 2020 09 22;16(9):e1008244. Epub 2020 Sep 22.

Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.

Alcoholic-related liver disease (ALD) is the cause of more than half of all liver-related deaths. Sustained excess drinking causes fatty liver and alcohol-related steatohepatitis, which may progress to alcoholic liver fibrosis (ALF) and eventually to alcohol-related liver cirrhosis (ALC). Unfortunately, it is difficult to identify patients with early-stage ALD, as these are largely asymptomatic. Consequently, the majority of ALD patients are only diagnosed by the time ALD has reached decompensated cirrhosis, a symptomatic phase marked by the development of complications as bleeding and ascites. The main goal of this study is to discover relevant upstream diagnoses helping to understand the development of ALD, and to highlight meaningful downstream diagnoses that represent its progression to liver failure. Here, we use data from the Danish health registries covering the entire population of Denmark during nineteen years (1996-2014), to examine if it is possible to identify patients likely to develop ALF or ALC based on their past medical history. To this end, we explore a knowledge discovery approach by using high-dimensional statistical and machine learning techniques to extract and analyze data from the Danish National Patient Registry. Consistent with the late diagnoses of ALD, we find that ALC is the most common form of ALD in the registry data and that ALC patients have a strong over-representation of diagnoses associated with liver dysfunction. By contrast, we identify a small number of patients diagnosed with ALF who appear to be much less sick than those with ALC. We perform a matched case-control study using the group of patients with ALC as cases and their matched patients with non-ALD as controls. Machine learning models (SVM, RF, LightGBM and NaiveBayes) trained and tested on the set of ALC patients achieve a high performance for data classification (AUC = 0.89). When testing the same trained models on the small set of ALF patients, their performance unsurprisingly drops a lot (AUC = 0.67 for NaiveBayes). The statistical and machine learning results underscore small groups of upstream and downstream comorbidities that accurately detect ALC patients and show promise in prediction of ALF. Some of these groups are conditions either caused by alcohol or caused by malnutrition associated with alcohol-overuse. Others are comorbidities either related to trauma and life-style or to complications to cirrhosis, such as oesophageal varices. Our findings highlight the potential of this approach to uncover knowledge in registry data related to ALD.
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http://dx.doi.org/10.1371/journal.pcbi.1008244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531835PMC
September 2020

Systems genetics analysis identifies calcium-signaling defects as novel cause of congenital heart disease.

Genome Med 2020 08 28;12(1):76. Epub 2020 Aug 28.

Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, DK-2200, Copenhagen, Denmark.

Background: Congenital heart disease (CHD) occurs in almost 1% of newborn children and is considered a multifactorial disorder. CHD may segregate in families due to significant contribution of genetic factors in the disease etiology. The aim of the study was to identify pathophysiological mechanisms in families segregating CHD.

Methods: We used whole exome sequencing to identify rare genetic variants in ninety consenting participants from 32 Danish families with recurrent CHD. We applied a systems biology approach to identify developmental mechanisms influenced by accumulation of rare variants. We used an independent cohort of 714 CHD cases and 4922 controls for replication and performed functional investigations using zebrafish as in vivo model.

Results: We identified 1785 genes, in which rare alleles were shared between affected individuals within a family. These genes were enriched for known cardiac developmental genes, and 218 of these genes were mutated in more than one family. Our analysis revealed a functional cluster, enriched for proteins with a known participation in calcium signaling. Replication in an independent cohort confirmed increased mutation burden of calcium-signaling genes in CHD patients. Functional investigation of zebrafish orthologues of ITPR1, PLCB2, and ADCY2 verified a role in cardiac development and suggests a combinatorial effect of inactivation of these genes.

Conclusions: The study identifies abnormal calcium signaling as a novel pathophysiological mechanism in human CHD and confirms the complex genetic architecture underlying CHD.
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http://dx.doi.org/10.1186/s13073-020-00772-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453558PMC
August 2020

Towards standardization guidelines for in silico approaches in personalized medicine.

J Integr Bioinform 2020 Jul 24;17(2-3). Epub 2020 Jul 24.

Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.
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http://dx.doi.org/10.1515/jib-2020-0006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756614PMC
July 2020

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study.

EBioMedicine 2020 Aug 4;58:102932. Epub 2020 Aug 4.

The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.

Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (T) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous T score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.

Findings: A higher T score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=-0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher T score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the T score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher T score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2.

Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health.

Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.
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http://dx.doi.org/10.1016/j.ebiom.2020.102932DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406914PMC
August 2020

Genetic variability in the absorption of dietary sterols affects the risk of coronary artery disease.

Eur Heart J 2020 07;41(28):2618-2628

Laekning, Medical Clinics, Lágmúli 5, 108 Reykjavik, Iceland.

Aims: To explore whether variability in dietary cholesterol and phytosterol absorption impacts the risk of coronary artery disease (CAD) using as instruments sequence variants in the ABCG5/8 genes, key regulators of intestinal absorption of dietary sterols.

Methods And Results: We examined the effects of ABCG5/8 variants on non-high-density lipoprotein (non-HDL) cholesterol (N up to 610 532) and phytosterol levels (N = 3039) and the risk of CAD in Iceland, Denmark, and the UK Biobank (105 490 cases and 844 025 controls). We used genetic scores for non-HDL cholesterol to determine whether ABCG5/8 variants confer greater risk of CAD than predicted by their effect on non-HDL cholesterol. We identified nine rare ABCG5/8 coding variants with substantial impact on non-HDL cholesterol. Carriers have elevated phytosterol levels and are at increased risk of CAD. Consistent with impact on ABCG5/8 transporter function in hepatocytes, eight rare ABCG5/8 variants associate with gallstones. A genetic score of ABCG5/8 variants predicting 1 mmol/L increase in non-HDL cholesterol associates with two-fold increase in CAD risk [odds ratio (OR) = 2.01, 95% confidence interval (CI) 1.75-2.31, P = 9.8 × 10-23] compared with a 54% increase in CAD risk (OR = 1.54, 95% CI 1.49-1.59, P = 1.1 × 10-154) associated with a score of other non-HDL cholesterol variants predicting the same increase in non-HDL cholesterol (P for difference in effects = 2.4 × 10-4).

Conclusions: Genetic variation in cholesterol absorption affects levels of circulating non-HDL cholesterol and risk of CAD. Our results indicate that both dietary cholesterol and phytosterols contribute directly to atherogenesis.
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http://dx.doi.org/10.1093/eurheartj/ehaa531DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377579PMC
July 2020
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