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Erik Ingelsson: Publication list

Summary

In total, 251 original research articles, 3 review articles, 3 book chapters, 4 editorials, 10 multi-center studies (listed collaborator), 10 other publications. Of the original research articles, I was first author on 33, and last author on 47.

53 original articles in journals with impact factor >30: 6 in JAMA, 29 in Nature Genetics, 9 in Nature, 4 in Lancet, 3 in New England Journal of Medicine, and 2 in Science. Of these publications, 4 were as first and 6 as last author.

36 original articles in journals with impact factor 10-30: 8 in Circulation, 5 in European Heart Journal, 3 in Journal of American College of Cardiology, 2 in Archives on Internal Medicine, 1 in JAMA Internal Medicine, 1 in Lancet Oncology, 1 in Lancet Diabetes & Endocrinology, 1 in Journal of Clinical Oncology, 1 in J Allergy Clin Immunol, 3 in PLoS Medicine, 3 in American Journal of Human Genetics, 5 in Nature Communications, 1 in Journal of Clinical Investigation and 1 in Molecular Psychiatry. Of these publications, I was first author on 10, and last author on 6.


Complete List of Published Work in MyBibliography: http://www.ncbi.nlm.nih.gov/sites/myncbi/erik.ingelsson.1/bibliography/47532577/public/?sort=date&direction=ascending

Bibliometry

From Web of Science / Google Scholar, updated February 28, 2017.

Sum of the times cited: 18,763 /30,124

Average citations per item: 63.18 / N.A.

h-index (the number of published papers with at least the same number of citations): 57 / 70

i10-index (the number of publications with at least 10 citations): N.A. / 189

Key contributions to science

1. My earliest contribution to science was within the epidemiology of congestive heart failure, a condition with high mortality that is often neglected in medical research. This work was done during and right after my PhD at Uppsala University in 2003 to 2006. The four papers included in my thesis, along with seven additional papers that I wrote as first name author focused on risk factors for congestive heart failure, and we contributed with much new knowledge in these studies as much less was known about risk factors for heart failure than for coronary heart disease and stroke. Three of the papers from this period were published in Journal of the American Medical Association (JAMA), but also several of the other papers were published in high-impact journals, such as Journal of the American College of Cardiology (JACC) and European Heart Journal. These papers got a lot of attention, and as a whole, the studies I worked with these years led to many new insights of the role of metabolic factors for the development of heart failure.

a. Ingelsson E, Sundström J, Ärnlöv J, Zethelius B, Lind L. Insulin Resistance and Risk of Congestive Heart Failure. JAMA. 2005; 294(3):334-41.

b. Ingelsson E, Riserus U, Berne C, Frystyk J, Flyvbjerg A, Axelsson T, Lundmark P, Zethelius B. Adiponectin and Risk of Congestive Heart Failure. JAMA. 2006; 295(15):1772-4.

c. Ingelsson E, Björklund-Bodegård K, Lind L, Ärnlöv J, Sundström J. Diurnal Blood Pressure Pattern and Risk of Congestive Heart Failure. JAMA. 2006; 295(24):2859-66.


2. Biomarkers and risk prediction belong to the very core of cardiovascular epidemiology, being key factors for improving health care and individualized treatment. I have been working extensively with prediction of CVD by use of both traditional and more novel biomarkers and by use of different statistical metrics for prediction. For example, the most influential paper from my post-doc at the Framingham Heart Study was a publication in JAMA, where we compared traditional lipids with apolipoproteins for risk prediction – a highly cited paper that had impact on guidelines for primary prevention of CVD from American Heart Association. This was also the first paper to apply net reclassification index (NRI), a risk prediction measure that has gained much popularity. I have also led development of new methods for risk prediction, such as application of various risk prediction metrics in a case-cohort setting. From 2011 and on, my group has changed focus going from analyses of one or a few biomarkers or variables at the time to analyses of hundreds to thousands of markers at once using -omics or other big data methods. A recent example is our study of thousands of metabolic features in relation to incident coronary heart disease where we identified four novel metabolites improving risk prediction. Another recent example of large-scale prediction is our paper in Lancet where we studied sex-specific associations of 655 measurements of demographics, health and lifestyle with all-cause mortality and six cause-specific mortality categories in 498,103 UK Biobank participants. Linked to this publication, we developed a hugely popular website, Ubble, disseminating our findings to researchers, policy-makers and the general public.

a. Ingelsson E, Schaefer EJ, Contois JH, McNamara JR, Sullivan L, Keyes MJ, Pencina MJ, Schoonmaker C, Wilson PW, D'Agostino RB, Vasan RS. Clinical Utility of Different Lipid Measures for Prediction of Coronary Heart Disease in Men and Women. JAMA. 2007; 298(7):776-85.

b. Ganna A, Reilly M, de Faire U, Pedersen N, Magnusson P, Ingelsson E. Risk Prediction Measures for Case-Cohort and Nested Case-Control Designs: An Application to Cardiovascular Disease. Am J Epidemiol. 2012; 175(7):715-24.

c. Ganna A, Salihovic S, Sundström J, Broeckling CD, Hedman ÅK, Magnusson PKE, Pedersen NL, Larsson A, Siegbahn A, Zilmer M, Prenni J, Ärnlöv J, Lind L, Fall T, Ingelsson E. Large-scale Metabolomic Profiling Identifies Novel Biomarkers for Incident Coronary Heart Disease. PLoS Genet. 2014;10(12):e1004801.

d. Ganna A, Ingelsson E. Five-year mortality predictors: A prospective study of ~500,000 UK Biobank participants. Lancet. 2015;386(9993):533-540.


3. The development in complex disease genetics has been remarkable in the past years starting off from the era of candidate gene-based studies with few consistently replicated genotype-phenotype associations to an exponentially increasing number of larger and larger genome-wide association study (GWAS) meta-analysis consortia, and now sequencing-based projects. My research group has led a number of projects having a large impact on the understanding of genetics of complex diseases. As a result of the work within GWAS consortia, we have published many papers in top-tier journals (amongst others 22 in Nature Genetics, six in Nature, and two in Science). I have had a leading role in many of these papers, as senior author, member of the writing group and/or steering committees. Most importantly, I was corresponding author of key papers identifying genetic loci associated with BMI, extreme obesity, circulating lipids, fasting glucose, insulin and 2-hour glucose. Our work has led to landmark papers dissecting the genetic architecture of complex traits - highlighting the very polygenetic nature of complex traits, the high degree of allelic heterogeneity, and that the genetics of extremes is similar to that of the full population - and discovering much new biology influencing these traits and giving leads to further in-depth characterization.

a. Speliotes EK*, Willer CJ*, Berndt SI*, Monda KL*, Thorleifsson G*, Jackson AU, [>300 authors], Barroso I, Boehnke M*, Stefansson K*, North KE*, McCarthy MI*, Hirschhorn JN*, Ingelsson E*, Loos RJ*. Association Analyses of 249,796 Individuals Reveal 18 New Loci Associated with Body Mass Index. Nat Genet. 2010; 42(11):937-48.

b. Scott RA*, Lagou V*, Welch RP*, Wheeler E, [>200 authors], Teslovich TM, Florez JC*, Langenberg C*, Ingelsson E*, Prokopenko I*, Barroso I*. Large-Scale Association Analyses Identify New Loci Influencing Glycemic Traits and Provide Insight into the Underlying Biological Pathways. Nat Genet. 2012; 44(9):991-1005.

c. Berndt SI*, Gustafsson S*, Mägi R*, Ganna A*, Wheeler E, [>300 authors], Scherag A, McCarthy MI*, Speliotes EK*, North KE*, Loos RJ*, Ingelsson E*. Genome-Wide Meta-Analysis Identifies 11 New Loci for Anthropometric Traits and Provides Insights into Genetic Architecture. Nat Genet. 2013; 45(5):501-12.

d. Willer CJ*, Schmidt EM*, Sengupta S*, Peloso GM, Gustafsson S, [>200 authors], Rich SS, Boehnke M*, Deloukas P*, Kathiresan S*, Mohlke KL*, Ingelsson E*, Abecasis GR*. Discovery and Refinement of Loci Associated with Lipid Levels. Nat Genet. 2013; 45(11):1274-83.

e. Locke AE*, Kahali B*, Berndt SI*, Justice AE*, Pers TH*, [>500 authors], North KE*, Ingelsson E*, Hirschhorn JN*, Loos RJ*, Speliotes EK*. Genetic Studies of Body Mass Index Yield New Insights for Obesity Biology. Nature. 2015; 518(7538):197-206.


4. With the availability of hundreds of robust genotype-phenotype associations, Mendelian randomization methods provide epidemiologists with an approach to infer causality avoiding reverse causation and confounding that have riddled observational studies in the past. I have led several large projects using these approaches, and further developing the methodology. Within the ENGAGE consortium, I led a large effort resulting in several papers in high-impact journals, studying obesity as a causal risk factor for various health outcomes. I was also the corresponding author of a paper indicating that adiponectin is causally related to insulin sensitivity, and the senior author on a paper describing the causal links between dyslipidemia and insulin sensitivity, insulin secretion and type 2 diabetes.

a. Fall T*, Hägg S*, Mägi R*, Ploner A, [>100 authors], Stefansson K, Pedersen NL*, McCarthy MI*, Ingelsson E*, Prokopenko I* for the European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium. The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis. PLOS Med. 2013; 10(6):e1001474.

b. Fall T*, Hägg S*, Ploner A, Mägi R, Fischer K, Draisma HHM, [>100 authors], Pedersen NL, Prokopenko I, McCarthy MI, Ingelsson E. Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors. Diabetes. 2015; 64(5):1841-52.

c. Hägg S, Fall T, Ploner A, Mägi R, [>50 authors], Prokopenko I, McCarthy MI, Pedersen NL, Ingelsson E. Adiposity as a Cause of Cardiovascular Disease: A Mendelian Randomization Study. Int J Epidemiol. 2015;44(2):578-86.

d. Gao H, Fall T, van Dam RM, Flyvbjerg A, Zethelius B, Ingelsson E*, Hägg S*. Evidence of a Causal Relationship between Adiponectin Levels and Insulin Sensitivity: A Mendelian Randomization Study. Diabetes. 2013; 62(4):1338-44.

e. Fall T*, Xie W*, Poon W, Yaghootkar H, Mägi R, GENESIS consortium, Knowles JW, Lyssenko V, Weedon M, Frayling TM*, Ingelsson E*. Using Genetic Variants to Assess the Relationship between Circulating Lipids and Type 2 Diabetes. Diabetes. 2015; 64(7):2676-84.


5. GWAS have provided us with hundreds of genetic loci robustly associated with metabolic and cardiovascular traits. However, for the majority of these, mechanisms are largely unknown. Over the past two years, I have refocused much of my research efforts towards identification and characterization of loci discovered in GWAS using a combination of in-depth studies in human (including various -omics methods), in vivo (in zebrafish) and in vitro studies (in adipocytes and myocytes). However, already in 2010, I published one of the first examples of a detailed characterization of GWAS signals (for fasting glucose and insulin), by use of refined physiological measures of glucose metabolism in humans, including intravenous measures of insulin sensitivity. We followed this up with similar study where we characterized all known loci associated with type 2 diabetes. Finally, I am one of the senior authors of a recent paper in JCI where we have identified a novel insulin resistance locus via GWAS, and then performed in vitro studies in adipocytes giving a detailed characterization of the biology underlying the signal. These studies serve as examples of the power of combining population-based data with functional follow-up studies.

a. Ingelsson E*, Langenberg C*, Hivert MF*, Prokopenko I, [60 authors], Watanabe RM, Florez JC. Detailed Physiologic Characterization Reveals Diverse Mechanisms for Novel Genetic Loci Regulating Glucose and Insulin Metabolism in Humans. Diabetes. 2010; 59(5):1266-75.

b. Dimas AS*, Lagou V*, Barker A*, Knowles JW*, Mägi R, [53 authors], Dupuis J, Watanabe RM*, Florez JC*, Ingelsson E*, McCarthy MI*, Prokopenko I* on behalf of the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators. Impact of Type 2 Diabetes Susceptibility Variants on Quantitative Glycemic Traits Reveals Mechanistic Heterogeneity. Diabetes. 2014; 63(6):2158-71.

c. Knowles JW*, Xie W*, Zhang Z*, Chennemsetty I*, [31 authors], Laakso M, Hao K*, Ingelsson E*, Frayling TM*, Weedon MN*, Walker M*, Quertermous T.* Identification and Validation of N-Acetyltransferase 2 as an Insulin Sensitivity Gene. J Clin Invest. 2015; 125(4):1739-51.

Asterisk denoted equal contribution.