Publications by authors named "Krista Fischer"

109 Publications

Development of the Gastrointestinal Dysfunction Score (GIDS) for critically ill patients - A prospective multicenter observational study (iSOFA study).

Clin Nutr 2021 08 18;40(8):4932-4940. Epub 2021 Jul 18.

Department of Anaesthesiology and Intensive Care, University of Tartu, Tartu, Estonia; Department of Anaesthesiology and Intensive Care, Tartu University Hospital, Tartu, Estonia.

Background & Aims: To develop a five grade score (0-4 points) for the assessment of gastrointestinal (GI) dysfunction in adult critically ill patients.

Methods: This prospective multicenter observational study enrolled consecutive adult patients admitted to 11 intensive care units in nine countries. At all sites, daily clinical data with emphasis on GI clinical symptoms were collected and intra-abdominal pressure measured. In five out of 11 sites, the biomarkers citrulline and intestinal fatty acid-binding protein (I-FABP) were measured additionally. Cox models with time-dependent scores were used to analyze associations with 28- and 90-day mortality. The models were estimated with stratification for study center.

Results: We included 540 patients (224 with biomarker measurements) with median age of 65 years (range 18-94), the Simplified Acute Physiology Score II score of 38 (interquartile range 26-53) points, and Sequential Organ Failure Assessment (SOFA) score of 6 (interquartile range 3-9) points at admission. Median ICU length of stay was 3 (interquartile range 1-6) days and 90-day mortality 18.9%. A new five grade Gastrointestinal Dysfunction Score (GIDS) was developed based on the rationale of the previously developed Acute GI Injury (AGI) grading. Citrulline and I-FABP did not prove their potential for scoring of GI dysfunction in critically ill. GIDS was independently associated with 28- and 90-day mortality when added to SOFA total score (HR 1.40; 95%CI 1.07-1.84 and HR 1.40; 95%CI 1.02-1.79, respectively) or to a model containing all SOFA subscores (HR 1.48; 95%CI 1.13-1.92 and HR 1.47; 95%CI 1.15-1.87, respectively), improving predictive power of SOFA score in all analyses.

Conclusions: The newly developed GIDS is additive to SOFA score in prediction of 28- and 90-day mortality. The clinical usefulness of this score should be validated prospectively.

Trial Registration: NCT02613000, retrospectively registered 24 November 2015.
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http://dx.doi.org/10.1016/j.clnu.2021.07.015DOI Listing
August 2021

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example.

J Pers Med 2021 Apr 29;11(5). Epub 2021 Apr 29.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands.

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.
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http://dx.doi.org/10.3390/jpm11050358DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145318PMC
April 2021

Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.

Nat Commun 2021 04 20;12(1):2337. Epub 2021 Apr 20.

Institute of Science and Technology Austria, Klosterneuburg, Austria.

While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.
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http://dx.doi.org/10.1038/s41467-021-22538-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058085PMC
April 2021

Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation.

mSystems 2021 Feb 16;6(1). Epub 2021 Feb 16.

Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia

The incidence of type 2 diabetes (T2D) has been increasing globally, and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long prediabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and prediabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome in Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4 year) period. Our results show that the inclusion of the gut microbiome improves prediction accuracy for modeling T2D-associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and nonlinear associations. Additionally, the modeling strategy carried out allowed us to compare the stability of model performance and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modeling strategies and the value of interpretable machine learning. Recent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider the microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes-associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using the microbiome in personalized medicine promising.
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http://dx.doi.org/10.1128/mSystems.01191-20DOI Listing
February 2021

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.

Circ Genom Precis Med 2020 12 13;13(6):e002769. Epub 2020 Aug 13.

The Usher Institute of Population Health Sciences & Informatics (A.D.M.), University of Edinburgh, Edinburgh, U.K.

Background: Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D).

Methods: To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D).

Results: None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background.

Conclusions: This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.
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http://dx.doi.org/10.1161/CIRCGEN.119.002769DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748049PMC
December 2020

Association of circulating metabolites in plasma or serum and risk of stroke: Meta-analysis from seven prospective cohorts.

Neurology 2020 Dec 2. Epub 2020 Dec 2.

Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands;

Objective: To conduct a comprehensive analysis of circulating metabolites and incident stroke in large prospective population-based settings.

Methods: We investigated the association of metabolites with risk of stroke in seven prospective cohort studies including 1,791 incident stroke events among 38,797 participants in whom circulating metabolites were measured by Nuclear Magnetic Resonance (H-NMR) technology. The relationship between metabolites and stroke was assessed using Cox proportional hazards regression models. The analyses were performed considering all incident stroke events and ischemic and hemorrhagic events separately.

Results: The analyses revealed ten significant metabolite associations. Amino acid histidine (hazard ratio (HR) per standard deviation (SD) = 0.90, 95% confidence interval (CI): 0.85, 0.94; = 4.45×10), glycolysis-related metabolite pyruvate (HR per SD = 1.09, 95% CI: 1.04, 1.14; = 7.45×10), acute phase reaction marker glycoprotein acetyls (HR per SD = 1.09, 95% CI: 1.03, 1.15; = 1.27×10), cholesterol in high-density lipoprotein (HDL) 2 and several other lipoprotein particles were associated with risk of stroke. When focusing on incident ischemic stroke, a significant association was observed with phenylalanine (HR per SD = 1.12, 95% CI: 1.05, 1.19; 4.13×10) and total and free cholesterol in large HDL particles.

Conclusions: We found association of amino acids, glycolysis-related metabolites, acute phase reaction markers, and several lipoprotein subfractions with the risk of stroke. These findings support the potential of metabolomics to provide new insights into the metabolic changes preceding stroke.
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http://dx.doi.org/10.1212/WNL.0000000000011236DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055347PMC
December 2020

Development and validation of two SCORE-based cardiovascular risk prediction models for Eastern Europe: a multicohort study.

Eur Heart J 2020 09;41(35):3325-3333

Department of Epidemiology & Public Health, University College London, 1-19 Torrington Place, London WC1E 7HB, UK.

Aims: Cardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model.

Methods And Results: We developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02-0.11] in the derivation cohort and 0.14 (95% CI 0.04-0.25) in the validation cohort.

Conclusion: Our recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.
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http://dx.doi.org/10.1093/eurheartj/ehaa571DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544536PMC
September 2020

Gallstones, Body Mass Index, C-Reactive Protein, and Gallbladder Cancer: Mendelian Randomization Analysis of Chilean and European Genotype Data.

Hepatology 2021 May;73(5):1783-1796

International Agency for Research on Cancer, World Health Organization, Lyon, France.

Background And Aims: Gallbladder cancer (GBC) is a neglected disease with substantial geographical variability: Chile shows the highest incidence worldwide, while GBC is relatively rare in Europe. Here, we investigate the causal effects of risk factors considered in current GBC prevention programs as well as C-reactive protein (CRP) level as a marker of chronic inflammation.

Approach And Results: We applied two-sample Mendelian randomization (MR) using publicly available data and our own data from a retrospective Chilean and a prospective European study. Causality was assessed by inverse variance weighted (IVW), MR-Egger regression, and weighted median estimates complemented with sensitivity analyses on potential heterogeneity and pleiotropy, two-step MR, and mediation analysis. We found evidence for a causal effect of gallstone disease on GBC risk in Chileans (P = 9 × 10 ) and Europeans (P = 9 × 10 ). A genetically elevated body mass index (BMI) increased GBC risk in Chileans (P = 0.03), while higher CRP concentrations increased GBC risk in Europeans (P = 4.1 × 10 ). European results suggest causal effects of BMI on gallstone disease (P = 0.008); public Chilean data were not, however, available to enable assessment of the mediation effects among causal GBC risk factors.

Conclusions: Two risk factors considered in the current Chilean program for GBC prevention are causally linked to GBC risk: gallstones and BMI. For Europeans, BMI showed a causal effect on gallstone risk, which was itself causally linked to GBC risk.
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http://dx.doi.org/10.1002/hep.31537DOI Listing
May 2021

Validating the doubly weighted genetic risk score for the prediction of type 2 diabetes in the Lifelines and Estonian Biobank cohorts.

Genet Epidemiol 2020 09 14;44(6):589-600. Epub 2020 Jun 14.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high-risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide polymorphism in the risk score, to correct for "Winner's curse" bias in the effect size estimates. The traditional (single-weighted genetic risk score; swGRS) and dwGRS were calculated for participants in Lifelines (n = 12,018) and EstBB (n = 34,129). The dwGRS was found to have stronger association with incident T2D (hazard ratio [HR] = 1.26 [95% confidence interval: 1.10-1.43] and HR = 1.35 [1.28-1.42]) compared to the swGRS (HR = 1.21 [1.07-1.38] and HR = 1.25 [1.19-1.32]) in Lifelines and EstBB, respectively. Comparing the 5-year predicted risks from the models with and without the dwGRS, the continuous net reclassification index was 0.140 (0.034-0.243; p = .009 Lifelines), and 0.257 (0.194-0.319; p < 2 × 10 EstBB). The dwGRS provided incremental value to the T2D prediction model with established phenotypic predictors. It clearly distinguished the risk groups for incident T2D in both biobanks thereby showing its clinical relevance.
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http://dx.doi.org/10.1002/gepi.22327DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7496366PMC
September 2020

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome.

Int J Obes (Lond) 2020 07 28;44(7):1596-1606. Epub 2020 May 28.

Department of Genetics, Harvard Medical School, Boston, MA, USA.

Background: Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.

Methods: We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS.

Results: We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms.

Conclusions: These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.
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http://dx.doi.org/10.1038/s41366-020-0603-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332400PMC
July 2020

Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals.

Nat Commun 2020 04 2;11(1):1628. Epub 2020 Apr 2.

Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia.

Polygenic Scores (PSs) describe the genetic component of an individual's quantitative phenotype or their susceptibility to diseases with a genetic basis. Currently, PSs rely on population-dependent contributions of many associated alleles, with limited applicability to understudied populations and recently admixed individuals. Here we introduce a combination of local ancestry deconvolution and partial PS computation to account for the population-specific nature of the association signals in individuals with admixed ancestry. We demonstrate partial PS to be a proxy for the total PS and that a portion of the genome is enough to improve susceptibility predictions for the traits we test. By combining partial PSs from different populations, we are able to improve trait predictability in admixed individuals with some European ancestry. These results may extend the applicability of PSs to subjects with a complex history of admixture, where current methods cannot be applied.
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http://dx.doi.org/10.1038/s41467-020-15464-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7118071PMC
April 2020

Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases.

Sci Adv 2020 02 19;6(8):eaax0301. Epub 2020 Feb 19.

MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.

Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation ( = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases.
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http://dx.doi.org/10.1126/sciadv.aax0301DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7030929PMC
February 2020

Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies.

Addict Biol 2021 01 16;26(1):e12880. Epub 2020 Feb 16.

Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany.

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [r ], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (r = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (r = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (r = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (r = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.
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http://dx.doi.org/10.1111/adb.12880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429266PMC
January 2021

ABCB1/4 gallbladder cancer risk variants identified in India also show strong effects in Chileans.

Cancer Epidemiol 2020 04 10;65:101643. Epub 2020 Feb 10.

International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France.

Background: The first large-scale genome-wide association study of gallbladder cancer (GBC) recently identified and validated three susceptibility variants in the ABCB1 and ABCB4 genes for individuals of Indian descent. We investigated whether these variants were also associated with GBC risk in Chileans, who show the highest incidence of GBC worldwide, and in Europeans with a low GBC incidence.

Methods: This population-based study analysed genotype data from retrospective Chilean case-control (255 cases, 2042 controls) and prospective European cohort (108 cases, 181 controls) samples consistently with the original publication.

Results: Our results confirmed the reported associations for Chileans with similar risk effects. Particularly strong associations (per-allele odds ratios close to 2) were observed for Chileans with high Native American (=Mapuche) ancestry. No associations were noticed for Europeans, but the statistical power was low.

Conclusion: Taking full advantage of genetic and ethnic differences in GBC risk may improve the efficiency of current prevention programs.
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http://dx.doi.org/10.1016/j.canep.2019.101643DOI Listing
April 2020

Associations of autozygosity with a broad range of human phenotypes.

Nat Commun 2019 10 31;10(1):4957. Epub 2019 Oct 31.

Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht University, Utrecht, 3584 CX, The Netherlands.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F) for >1.4 million individuals, we show that F is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F are confirmed within full-sibling pairs, where the variation in F is independent of all environmental confounding.
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http://dx.doi.org/10.1038/s41467-019-12283-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823371PMC
October 2019

Circulating glucuronic acid predicts healthspan and longevity in humans and mice.

Aging (Albany NY) 2019 09 26;11(18):7694-7706. Epub 2019 Sep 26.

BIOAGE, Richmond, CA 94804, USA.

Glucuronic acid is a metabolite of glucose that is involved in the detoxification of xenobiotic compounds and the structure/remodeling of the extracellular matrix. We report for the first time that circulating glucuronic acid is a robust biomarker of mortality that is conserved across species. We find that glucuronic acid levels are significant predictors of all-cause mortality in three population-based cohorts from different countries with 4-20 years of follow-up (HR=1.44, p=2.9×10 in the discovery cohort; HR=1.13, p=0.032 and HR=1.25, p=0.017, respectively in the replication cohorts), as well as in a longitudinal study of genetically heterogenous mice (HR=1.29, p=0.018). Additionally, we find that glucuronic acid levels increase with age and predict future healthspan-related outcomes. Together, these results demonstrate glucuronic acid as a robust biomarker of longevity and healthspan.
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http://dx.doi.org/10.18632/aging.102281DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781977PMC
September 2019

A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals.

Nat Commun 2019 08 20;10(1):3346. Epub 2019 Aug 20.

Division of Human Nutrition, Wageningen University, PO Box 17, 6700 AA, Wageningen, The Netherlands.

Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.
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http://dx.doi.org/10.1038/s41467-019-11311-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6702196PMC
August 2019

Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium.

Sci Rep 2019 08 12;9(1):11623. Epub 2019 Aug 12.

Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.

Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10), methionine (p-value = 9.2 × 10), tyrosine (p-value = 2.1 × 10), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.
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http://dx.doi.org/10.1038/s41598-019-47282-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6690953PMC
August 2019

Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa.

Nat Genet 2019 08 15;51(8):1207-1214. Epub 2019 Jul 15.

Clinical Genetics Unit, Department of Woman and Child Health, University of Padova, Padova, Italy.

Characterized primarily by a low body-mass index, anorexia nervosa is a complex and serious illness, affecting 0.9-4% of women and 0.3% of men, with twin-based heritability estimates of 50-60%. Mortality rates are higher than those in other psychiatric disorders, and outcomes are unacceptably poor. Here we combine data from the Anorexia Nervosa Genetics Initiative (ANGI) and the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) and conduct a genome-wide association study of 16,992 cases of anorexia nervosa and 55,525 controls, identifying eight significant loci. The genetic architecture of anorexia nervosa mirrors its clinical presentation, showing significant genetic correlations with psychiatric disorders, physical activity, and metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of common variants associated with body-mass index. These results further encourage a reconceptualization of anorexia nervosa as a metabo-psychiatric disorder. Elucidating the metabolic component is a critical direction for future research, and paying attention to both psychiatric and metabolic components may be key to improving outcomes.
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http://dx.doi.org/10.1038/s41588-019-0439-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779477PMC
August 2019

Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification.

BMC Cancer 2019 Jun 10;19(1):557. Epub 2019 Jun 10.

Estonian Genome Center, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia.

Background: Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.

Methods: Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts.

Results: The metaGRS that combines two genetic risk scores (metaGRS - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS.

Conclusions: We have shown that metaGRS that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.
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http://dx.doi.org/10.1186/s12885-019-5783-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558751PMC
June 2019

Food neophobia associates with poorer dietary quality, metabolic risk factors, and increased disease outcome risk in population-based cohorts in a metabolomics study.

Am J Clin Nutr 2019 07;110(1):233-245

Genomics and Biomarkers Unit, National Institute for Health and Welfare, Helsinki, Finland.

Background: Food neophobia is considered a behavioral trait closely linked to adverse eating patterns and reduced dietary quality, which have been associated with increased risk of obesity and noncommunicable diseases.

Objectives: In a cross-sectional and prospective study, we examined how food neophobia is associated with dietary quality, health-related biomarkers, and disease outcome incidence in Finnish and Estonian adult populations.

Methods: The study was conducted based on subsamples of the Finnish DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) cohort (n = 2982; age range: 25-74 y) and the Estonian Biobank cohort (n = 1109; age range: 18-83 y). The level of food neophobia was assessed using the Food Neophobia Scale, dietary quality was evaluated using the Baltic Sea Diet Score (BSDS), and biomarker profiles were determined using an NMR metabolomics platform. Disease outcome information was gathered from national health registries. Follow-up data on the NMR-based metabolomic profiles and disease outcomes were available in both populations.

Results: Food neophobia associated significantly (adjusted P < 0.05) with health-related biomarkers [e.g., ω-3 (n-3) fatty acids, citrate, α1-acid glycoprotein, HDL, and MUFA] in the Finnish DILGOM cohort. The significant negative association between the severity of food neophobia and ω-3 fatty acids was replicated in all cross-sectional analyses in the Finnish DILGOM and Estonian Biobank cohorts. Furthermore, food neophobia was associated with reduced dietary quality (BSDS: β: -0.03 ± 0.006; P = 8.04 × 10-5), increased fasting serum insulin (β: 0.004 ± 0.0013; P = 5.83 × 10-3), and increased risk of type 2 diabetes during the ∼8-y follow-up (HR: 1.018 ± 0.007; P = 0.01) in the DILGOM cohort.

Conclusions: In the Finnish and Estonian adult populations, food neophobia was associated with adverse alteration of health-related biomarkers and risk factors that have been associated with an increased risk of noncommunicable diseases. We also found that food neophobia associations with ω-3 fatty acids and associated metabolites are mediated through dietary quality independent of body weight.
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http://dx.doi.org/10.1093/ajcn/nqz100DOI Listing
July 2019

Parents of early-maturing girls die younger.

Evol Appl 2019 Jun 23;12(5):1050-1061. Epub 2019 Mar 23.

Institute of Veterinary Medicine and Animal Sciences Estonian University of Life Sciences Tartu Estonia.

According to the life-history theory, rates of sexual maturation have coevolved with mortality rates so that individuals who mature faster tend to die younger. We used two data sets, providing different markers for the speed of pubertal development to test whether rates of sexual maturation of women predict the age at death of their parents. In the data set of Estonian schoolgirls born between 1936 and 1961, the rate of breast development predicted lifespan of both mothers and fathers (irrespectively of their socio-economic position), so that parents of rapidly maturing girls died at younger age. This finding supports the view that fast maturation rates in humans have coevolved with short lifespans and that such trade-offs can be detected as intergenerational phenotypic correlations in modern populations. Menarcheal age of participants of Estonian Biobank (born between 1925 and 1996) did not predict the age of death of their mothers; however, it did predict survival of their fathers, but only in environment where the genetic variation is exposed (families where at least one parent had tertiary education). In such families (where girls also matured 0.2-0.4 years earlier than in poorly educated families), 1-year delay in daughter's menarche corresponded to 9% lower hazard of father's death. Heritability of menarcheal age was also highest in well-educated families. The latter findings are consistent with the idea that genetic differences in the rate of pubertal maturation may be expressed most clearly in well-off families because in such families, the contribution of environmental variance to total phenotypic variance in menarcheal age is smallest. Our findings suggest that with global improvement and equalization of growth conditions, reductions of environmental variation in the rate of maturation increasingly expose the genetic differences in menarcheal age to selection. Under such conditions, selection on menarcheal age has a potential to affect the evolution of lifespan.
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http://dx.doi.org/10.1111/eva.12780DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503892PMC
June 2019

Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances.

Elife 2019 01 15;8. Epub 2019 Jan 15.

Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near , , , , , and 13q21.31, and identify and replicate novel findings near , , and . We also validate previous findings near 5q33.3/ and , whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer - but not other cancers - explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles.

Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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http://dx.doi.org/10.7554/eLife.39856DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333444PMC
January 2019

PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data.

PLoS Comput Biol 2019 01 14;15(1):e1006734. Epub 2019 Jan 14.

Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America.

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70-88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.
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http://dx.doi.org/10.1371/journal.pcbi.1006734DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347288PMC
January 2019

Estimating the performance of three cardiovascular disease risk scores: the Estonian Biobank cohort study.

J Epidemiol Community Health 2019 03 11;73(3):272-277. Epub 2019 Jan 11.

Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia.

Background: We aim to investigate the predictive ability of PCE (Pooled Cohort Equations), QRISK2 and SCORE (Systematic COronary Risk Estimation) scoring systems for atherosclerotic cardiovascular disease (ASCVD) risk prediction in Estonia, a country with one of the highest ASCVD event rates in Europe.

Methods: Seven-year risk estimates were calculated in risk score-specific subsets of the Estonian Biobank cohort. Calibration was assessed by standardised incidence ratios (SIRs) and discrimination by Harrell's C-statistics. In addition, a head-to-head comparison of the scores was performed in the intersection of the three score-specific subcohorts.

Results: PCE, QRISK2 and SCORE risk estimates were calculated for 4356, 7191 and 3987 eligible individuals, respectively. During the 7-year follow-up, 220 hard ASCVD events (PCE outcome), 671 ASCVD events (QRISK2 outcome) and 94 ASCVD deaths (SCORE outcome) occurred among the score-specific subsets of the cohort. While PCE (SIR 1.03, 95% CI 0.90 to 1.18) and SCORE (SIR 0.99, 95% CI 0.81 to 1.21) were calibrated well for the cohort, QRISK2 underestimated the risk by 48% (SIR 0.52, 95% CI 0.48 to 0.56). In terms of discrimination, PCE (C-statistic 0.778) was inferior to QRISK2 (C-statistic 0.812) and SCORE (C-statistic 0.865). All three risk scores performed at similar level in the head-to-head comparison.

Conclusion: Of three widely used ASCVD risk scores, PCE and SCORE performed at acceptable level, while QRISK2 underestimated ASCVD risk markedly. These results highlight the need for evaluating the accuracy of ASCVD risk scores prior to use in high-risk populations.
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http://dx.doi.org/10.1136/jech-2017-209965DOI Listing
March 2019

Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adults.

EBioMedicine 2018 Dec 13;38:206-216. Epub 2018 Nov 13.

Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, The Netherlands.

Background: DNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health.

Methods: We meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n = 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), diastolic, and systolic blood pressure (BP).

Findings: Lower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P < 0·012. In contrast, lower DNA methylation at cg09935388, the strongest adult own smoking locus, was associated with decreased BMI, WC, and BP (adjusted 1 × 10 < P < 0.01). Similarly, lower DNA methylation at cg12876356, cg18316974, cg09662411, and cg18146737 was associated with decreased BMI and WC (5 × 10 < P < 0.001). Lower DNA methylation at all the CpGs was consistently associated with higher TG levels.

Interpretation: Epigenetic changes at the GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors. FUND: European Union's Horizon 2020 research and innovation programme under grant agreement no. 633595 DynaHEALTH.
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http://dx.doi.org/10.1016/j.ebiom.2018.10.066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306313PMC
December 2018

Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders.

Am J Hum Genet 2018 11;103(5):691-706

Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.
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http://dx.doi.org/10.1016/j.ajhg.2018.09.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218410PMC
November 2018

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.

Nat Genet 2018 11 8;50(11):1505-1513. Epub 2018 Oct 8.

Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).
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http://dx.doi.org/10.1038/s41588-018-0241-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287706PMC
November 2018

A meta-analysis of Italian and Estonian individuals shows an effect of common variants in on blood apoB levels.

Biomark Med 2019 08 7;13(11):931-940. Epub 2018 Sep 7.

Genomics of Common Disease, Division of Diabetes, Endocrinology & Metabolism, Department of Medicine, Imperial College London, London, UK.

The aim of the study was to explore the effects of variants at on a range of cardio-metabolic phenotypes. We analyzed the range of variants within Genetics in Brisighella Health Study and genes using an additive genetic model on 18 cardiometabolic phenotypes in a sample of 1645 individuals from the Genetics in Brisighella Health Study and replicated in 10,662 individuals from the Estonian Genome Center University of Tartu. We defined directly the effects of rs3846662:C>A at on apoB levels. The analysis also confirmed effects of on low-density lipoprotein-cholesterol and total cholesterol levels. Variants in gene did not reveal any associations with cardiometabolic phenotypes. This study highlights effect of locus on assay-determined apoB levels, an infrequent measure of blood lipids in large studies.
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http://dx.doi.org/10.2217/bmm-2017-0431DOI Listing
August 2019
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