Dr Ayse Demirkan

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

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NEDD4-2 alias NEDD4L (neural precursor cell expressed, developmentally downregulated) gene was reported as a candidate gene for epileptic photo-sensitivity. We aimed to investigate this possible association of NEDD4-2 variants with idiopathic photosensitive epilepsy. Consecutive patients who had been followed up at our epilepsy center and diagnosed with idiopathic epilepsy according to ILAE criteria and clear-cut photoparoxysmal responses in their electroencephalograms and 100 ethnically matched healthy subjects were included in the study. The regions around previously reported three variants, namely, S233L, E271A and H515P were tracked with DHPLC and the samples showing variations were sequenced. 81 patients (63 females) aged between 12-63 years (45 had juvenile myoclonic epilepsy, 11 childhood absence epilepsy, 14 juvenile absence epilepsy, 7 late onset idiopathic generalized epilepsy, 1 unclassified idiopathic generalized epilepsy, and 3 patients with idiopathic photosensitive occipital lobe epilepsy) were included in this study. We found only one heterozygous S233L variant in a 23-year-old man who has photosensitive form of juvenile absence epilepsy and pattern sensitivity to striped carpets. Other two variants were not found in any of the other patients and controls. Our results suggest that three screened NEDD4-2 variants do not play a leading role in the pathogenesis of photosensitive epilepsy in the Turkish population.

Purpose: Genetic and epidemiologic studies have shown that lipid genes and high-density lipoproteins (HDLs) are implicated in age-related macular degeneration (AMD). We studied circulating lipid levels in relationship to AMD in a large European dataset. Design: Pooled analysis of cross-sectional data. Participants: Individuals (N = 30 953) aged 50 years or older participating in the European Eye Epidemiology (E3) consortium and 1530 individuals from the Rotterdam Study with lipid subfraction data. Methods: AMD features were graded on fundus photographs using the Rotterdam classification. Routine blood lipid measurements, genetics, medication, and potential confounders were extracted from the E3 database. In a subgroup of the Rotterdam Study, lipid subfractions were identified by the Nightingale biomarker platform. Random-intercepts mixed-effects models incorporating confounders and study site as a random effect were used to estimate associations. Main Outcome Measures: AMD features and stage; lipid measurements. Results: HDL was associated with an increased risk of AMD (odds ratio [OR], 1.21 per 1-mmol/l increase; 95% confidence interval [CI], 1.14-1.29), whereas triglycerides were associated with a decreased risk (OR, 0.94 per 1-mmol/l increase; 95% CI, 0.91-0.97). Both were associated with drusen size. Higher HDL raised the odds of larger drusen, whereas higher triglycerides decreases the odds. LDL cholesterol reached statistical significance only in the association with early AMD (P = 0.045). Regarding lipid subfractions, the concentration of extra-large HDL particles showed the most prominent association with AMD (OR, 1.24; 95% CI, 1.10-1.40). The cholesteryl ester transfer protein risk variant (rs17231506) for AMD was in line with increased HDL levels (P = 7.7 x 10(-7)), but lipase C risk variants (rs2043085, rs2070895) were associated in an opposite way (P = 1.0 x 10(-6) and P = 1.6 x 10(-4)). Conclusions: Our study suggested that HDL cholesterol is associated with increased risk of AMD and that triglycerides are negatively associated. Both show the strongest association with early AMD and drusen. Extra-large HDL subfractions seem to be drivers in the relationship with AMD, and variants in lipid genes play a more ambiguous role in this association. Whether systemic lipids directly influence AMD or represent lipid metabolism in the retina remains to be answered. (C) 2018 by the American Academy of Ophthalmology

Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 x 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.

Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA-targeted intervention to reduce cardiovascular risk.

Major depression (MD) is one of the most prevalent psychiatric disorders and a leading cause of loss in work productivity. A combination of genetic and environmental risk factors probably contributes to MD. We present data from a genome-wide association study revealing a neuron-specific neutral amino acid transporter (SLC6A15) as a susceptibility gene for MD. Risk allele carrier status in humans and chronic stress in mice were associated with a down-regulation of the expression of this gene in the hippocampus, a brain region implicated in the pathophysiology of MD. The same polymorphisms also showed associations with alterations in hippocampal volume and neuronal integrity. Thus, decreased SLC6A15 expression, due to genetic or environmental factors, might alter neuronal circuits related to the susceptibility for MD. Our convergent data from human genetics, expression studies, brain imaging, and animal models suggest a pathophysiological mechanism for MD that may be accessible to drug targeting.

Chronic kidney disease (CKD), impairment of kidney function, is a serious public health problem, and the assessment of genetic factors influencing kidney function has substantial clinical relevance. Here, we report a meta-analysis of genomewide association studies for kidney function-related traits, including 71,149 east Asian individuals from 18 studies in 11 population-, hospital- or family-based cohorts, conducted as part of the Asian Genetic Epidemiology Network (AGEN). Our meta-analysis identified 17 loci newly associated with kidney function-related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and estimated glomerular filtration rate based on serum creatinine levels (eGFRcrea) (P < 5.0 x 10(-8)). We further examined these loci with in silico replication in individuals of European ancestry from the KidneyGen, CKDGen and GUGC consortia, including a combined total of similar to 110,347 individuals. We identify pleiotropic associations among these loci with kidney function-related traits and risk of CKD. These findings provide new insights into the genetics of kidney function.

Variation in body iron is associated with or causes diseases, including anaemia and iron overload. Here, we analyse genetic association data on biochemical markers of iron status from 11 European-population studies, with replication in eight additional cohorts (total up to 48,972 subjects). We find 11 genome-wide-significant (P

The first generation of genome-wide association studies (GWA studies) for psychiatric disorders has led to new insights regarding the genetic architecture of these disorders. We now start to realize that a larger number of genes, each with a small contribution, are likely to explain the heritability of psychiatric diseases. The contribution of a large number of genes to complex traits can be investigated with genome-wide profiling. In a discovery sample a genetic risk profile for depression was defined based on a GWA study of 1738 adult cases and 1802 controls. The genetic risk scores were tested in two population based samples of elderly participants. The genetic risk profiles were evaluated for depression and anxiety in the Rotterdam Study cohort and the Erasmus Rucphen Family (ERF) study. The genetic risk scores were significantly associated to different measures of depression and explained up to ~0.7% of the variance in depression in Rotterdam Study and up to ~1 % in ERF study. The genetic score for depression was also significantly associated to anxiety explaining up to 2.1% in Rotterdam study. These findings suggest the presence of many genetic loci of small effect that influence both depression and anxiety. Remarkably, the predictive value of these profiles was as large in the sample of elderly participants as in the middle-aged samples.

Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes. H-1 Nuclear Magnetic Resonance (H-1-NMR) spectroscopy experiments yielded successful quantification of 42 unique metabolites in 2,482 individuals from The Erasmus Rucphen Family (ERF) study. Heritability of metabolites were estimated by SOLAR. GWAS was performed by linear mixed models, using HapMap imputations. Based on physical vicinity and pathway analyses, candidate genes were screened for coding region variation using exome sequence data. Heritability estimates for metabolites ranged between 10% and 52%. GWAS replicated three known loci in the metabolome wide significance: CPS1 with glycine (P-value = 1.27x10(-32)), PRODH with proline (P-value = 1.11x10(-19)), SLC16A9 with carnitine level (P-value = 4.81x10(-14)) and uncovered a novel association between DMGDH and dimethyl-glycine (P-value = 1.65x10(-19)) level. In addition, we found three novel, suggestively significant loci: TNP1 with pyruvate (P-value = 1.26x10(-8)), KCNJ16 with 3-hydroxybutyrate (P-value = 1.65x10(-8)) and 2p12 locus with valine (P-value = 3.49x10(-8)). Exome sequence analysis identified potentially causal coding and regulatory variants located in the genes CPS1, KCNJ2 and PRODH, and revealed allelic heterogeneity for CPS1 and PRODH. Combined GWAS and exome analyses of metabolites detected by high-resolution H-1-NMR is a robust approach to uncover metabolite quantitative trait loci (mQTL), and the likely causative variants in these loci. It is anticipated that insight in the genetics of intermediate phenotypes will provide additional insight into the genetics of complex traits.

Metabolic dysregulation in multiple tissues alters glucose homeostasis and influences risk for type 2 diabetes (T2D). To identify pathways and tissues influencing T2D-relevant glycemic traits (fasting glucose [FG], fasting insulin [FI], two-hour glucose [2hGlu] and glycated hemoglobin [HbA1c]), we investigated associations of exome-array variants in up to 144,060 individuals without diabetes of multiple ancestries. Single-variant analyses identified novel associations at 21 coding variants in 18 novel loci, whilst gene-based tests revealed signals at two genes, TF (HbA1c) and G6PC (FG, FI). Pathway and tissue enrichment analyses of trait-associated transcripts confirmed the importance of liver and kidney for FI and pancreatic islets for FG regulation, implicated adipose tissue in FI and the gut in 2hGlu, and suggested a role for the non-endocrine pancreas in glucose homeostasis. Functional studies demonstrated that a novel FG/FI association at the liver-enriched G6PC transcript was driven by multiple rare loss-of-function variants. The FG/HbA1c-associated, islet-specific G6PC2 transcript also contained multiple rare functional variants, including two alleles within the same codon with divergent effects on glucose levels. Our findings highlight the value of integrating genomic and functional data to maximize biological inference.

Serum concentrations of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs) and total cholesterol (TC) are important heritable risk factors for cardiovascular disease. Although genome-wide association studies (GWASs) of circulating lipid levels have identified numerous loci, a substantial portion of the heritability of these traits remains unexplained. Evidence of unexplained genetic variance can be detected by combining multiple independent markers into additive genetic risk scores. Such polygenic scores, constructed using results from the ENGAGE Consortium GWAS on serum lipids, were applied to predict lipid levels in an independent population-based study, the Rotterdam Study-II (RS-II). We additionally tested for evidence of a shared genetic basis for different lipid phenotypes. Finally, the polygenic score approach was used to identify an alternative genome-wide significance threshold before pathway analysis and those results were compared with those based on the classical genome-wide significance threshold. Our study provides evidence suggesting that many loci influencing circulating lipid levels remain undiscovered. Cross-prediction models suggested a small overlap between the polygenic backgrounds involved in determining LDL-C, HDL-C and TG levels. Pathway analysis utilizing the best polygenic score for TC uncovered extra information compared with using only genome-wide significant loci. These results suggest that the genetic architecture of circulating lipids involves a number of undiscovered variants with very small effects, and that increasing GWAS sample sizes will enable the identification of novel variants that regulate lipid levels. European Journal of Human Genetics (2011) 19, 813-819; doi:10.1038/ejhg.2011.21; published online 30 March 2011

Elevated serum urate concentrations can cause gout, a prevalent and painful inflammatory arthritis. By combining data from >140,000 individuals of European ancestry within the Global Urate Genetics Consortium (GUGC), we identified and replicated 28 genome-wide significant loci in association with serum urate concentrations (18 new regions in or near TRIM46, INHBB, SEMBT1, TMEM171, VEGFA, BAZ1B, PRKAG2, STC1, HNF4G, A1CF, ATXN2, UBE2Q2, IGF1R, NFAT5, MAF, HLF, ACVR1B-ACVRL1 and B3GNT4). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. We further characterized these loci for associations with gout, transcript expression and the fractional excretion of urate. Network analyses implicate the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. New candidate genes for serum urate concentration highlight the importance of metabolic control of urate production and excretion, which may have implications for the treatment and prevention of gout.

Background: Lipids are increasingly involved in cardiovascular risk prediction as potential proarrhythmic influencers. However, knowledge is limited about the specific mechanisms connecting lipid alterations with atrial conduction. Methods: To shed light on this issue, we conducted a broad assessment of 151 sphingo- and phospholipids, measured using mass spectrometry, for association with atrial conduction, measured by P wave duration (PWD) from standard electrocardiograms, in the MICROS study (Microisolates in South Tyrol) (n=839). Causal pathways involving lipidomics, body mass index (BMI), and PWD were assessed using 2-sample Mendelian randomization analyses based on published genome-wide association studies of lipidomics (n=4034) and BMI (n=734 481), and genetic association analysis of PWD in 5 population-based studies (n=24 236). Results: We identified an association with relative phosphatidylcholine 38:3 (%PC 38:3) concentration, which was replicated in the ORCADES (Orkney Complex Disease Study; n=951), with a pooled association across studies of 2.59 (95% CI, 1.3-3.9; P=1.1x10(-4)) ms PWD per mol% increase. While being independent of cholesterol, triglycerides, and glucose levels, the %PC 38:3-PWD association was mediated by BMI. Results supported a causal effect of BMI on both PWD (P=8.3x10(-5)) and %PC 38:3 (P=0.014). Conclusions: Increased %PC 38:3 levels are consistently associated with longer PWD, partly because of the confounding effect of BMI. The causal effect of BMI on PWD reinforces evidence of BMI's involvement into atrial electrical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.

Objective Metabolic syndrome (MS) is associated with an increased risk of coronary artery disease (CAD) and type 2 diabetes mellitus (DM). In MS, adipose tissue has been shown to function as a paracrine and an endocrine organ secreting various adipocytokines. In the current study, adiponectin, tumor necrosis factor-alpha (TNF-alpha) and leptin gene expressions in the epicardial adipose tissue (EAT), paracardial adipose tissue (PAT) and subcutaneous adipose tissue (SAT) were investigated in MS patients with CAD and in non-MS patients without CAD. Methods and Results Thirty-seven patients with MS undergoing coronary artery bypass grafting due to CAD (MS group) and twenty-three non-MS patients without CAD undergoing heart valve surgery (control group) were recruited prospectively to the study. Relative gene expressions of adiponectin, TNF-alpha and leptin in EAT, PAT and SAT were compared between two groups of patients. Adiponectin gene expression in EAT and PAT were significantly lower in MS group compared to the control group (p < 0.0001, p=0.04, respectively) while SAT adiponectin gene expression did not differ significantly (p=0.64). TNF-alpha and leptin gene expressions were found to be statistically significantly higher in EAT, PAT and SAT of the MS group (p < 0.0001, for all). Conclusion Our results demonstrate that TNF-alpha and leptin gene expressions increase prominently in the EAT, PAT and SAT while adiponectin gene expression decreases significantly in EAT and PAT in MS patients with CAD. These findings suggest that disturbances in expression of adiponectin, TNF-alpha and leptin in EAT, PAT and SAT might play an important role in MS patients with CAD.

Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P

BACKGROUND: The datafiles deposited here are products of the research project "Linking the gut microbiome to host DNA methylation by a discovery and replication epigenome-wide association study" Authors: Ayşe Demirkan1,2, Jenny van Dongen3,4, Casey T. Finnicum5, Harm-Jan Westra1, Soesma Jankipersadsing1, Gonneke Willemsen3,4, Richard G. Ijzerman6, Dorret I. Boomsma3,4, Erik A. Ehli5, Marc Jan Bonder1, Jingyuan Fu,1,7 Lude Franke1, Cisca Wijmenga1, Eco J.C. de Geus3,4, Alexander Kurilshikov1, Alexandra Zhernakova1 1 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands 2 Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom 3 Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands 4 Amsterdam Public Health Research Institute, Amsterdam, the Netherlands 5 Avera Institute of Human Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD, USA 6 Department of Endocrinology, Amsterdam University Medical Center, location VUMC, Amsterdam, the Netherlands 7 Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands Corresponding Authors: Alexandra Zhernakova; Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands Ayse Demirkan; Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom. FILES:  1-merged_lld16s.Rata: Epigenome-wide association of 16s microbial abundances in LifeLines-Deep (LLD, n = 616, 450k methylation array)  2-lld_mgs.Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived taxa relative abundances (n = 683, 450k methylation array)) 3-lld_mgs_pathways. Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived bacterial pathway relative abundances (n = 683, 450k methylation array) FUNDING: The Lifelines initiative has been made possible by subsidy from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), Groningen University and the Provinces in the North of the Netherlands (Drenthe, Friesland, Groningen). The Netherlands Twin Register acknowledges funding from the Netherlands Organization for Scientific Research (NWO): (NWO 911–09–032; NWO 480-04-004; 480-15-001/674, NWO 916-130-82), Biobanking and Biomolecular Research Infrastructure (184.033.111),  and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), the European Research Council (Genetics of Mental Illness 230374), the European Research Council (Genetics of Mental Illness 230374), and INRA-Pfizer. Pfizer provided support for data collection, but did not have any additional role in the study design, data analysis, decision to publish, or preparation of the manuscript.  

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in upto 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 x 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for similar to 2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous systemin obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Increases in throughput and decreases in costs have facilitated large scale metabolomics studies, the simultaneous measurement of large numbers of biochemical components in biological samples. Initial large scale studies focused on biomarker discovery for disease or disease progression and helped to understand biochemical pathways underlying disease. The first population-based studies that combined metabolomics and genome wide association studies (mGWAS) have increased our understanding of the (genetic) regulation of biochemical conversions. Measurements of metabolites as intermediate phenotypes are a potentially very powerful approach to uncover how genetic variation affects disease susceptibility and progression. However, we still face many hurdles in the interpretation of mGWAS data. Due to the composite nature of many metabolites, single enzymes may affect the levels of multiple metabolites and, conversely, levels of single metabolites may be affected by multiple enzymes. Here, we will provide a global review of the current status of mGWAS. We will specifically discuss the application of prior biological knowledge present in databases to the interpretation of mGWAS results and discuss the potential of mathematical models. As the technology continuously improves to detect metabolites and to measure genetic variation, it is clear that comprehensive systems biology based approaches are required to further our insight in the association between genes, metabolites and disease. This article is part of a Special Issue entitled: From Genome to Function. (C) 2014 Elsevier B.V. All rights reserved.

Depression is a heritable trait that exists on a continuum of varying severity and duration. Yet, the search for genetic variants associated with depression has had few successes. We exploit the entire continuum of depression to find common variants for depressive symptoms. In this genome-wide association study, we combined the results of 17 population-based studies assessing depressive symptoms with the Center for Epidemiological Studies Depression Scale. Replication of the independent top hits (p

The genetics of depression has been explored in genome-wide association studies that focused on either major depressive disorder or depressive symptoms with mostly negative findings. A broad depression phenotype including both phenotypes has not been tested previously using a genome-wide association approach. We aimed to identify genetic polymorphisms significantly associated with a broad phenotype from depressive symptoms to major depressive disorder. We analyzed two prior studies of 70,017 participants of European ancestry from general and clinical populations in the discovery stage. We performed a replication meta-analysis of 28,328 participants. Single nucleotide polymorphism (SNP)-based heritability and genetic correlations were calculated using linkage disequilibrium score regression. Discovery and replication analyses were performed using a p-value-based meta-analysis. Lifetime major depressive disorder and depressive symptom scores were used as the outcome measures. The SNP-based heritability of major depressive disorder was 0.21 (SE = 0.02), the SNP-based heritability of depressive symptoms was 0.04 (SE = 0.01), and their genetic correlation was 1.001 (SE = 0.2). We found one genome-wide significant locus related to the broad depression phenotype (rs9825823, chromosome 3: 61,082,153, p = 8.2 × 10–9) located in an intron of the FHIT gene. We replicated this SNP in independent samples (p = .02) and the overall meta-analysis of the discovery and replication cohorts (1.0 × 10–9). This large study identified a new locus for depression. Our results support a continuum between depressive symptoms and major depressive disorder. A phenotypically more inclusive approach may help to achieve the large sample sizes needed to detect susceptibility loci for depression.

OBJECTIVEMetformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin.RESEARCH DESIGN AND METHODSWe analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways.RESULTSWe found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target.CONCLUSIONSOur results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease.

Scavenger receptor BI (SR-BI) is the major receptor for high-density lipoprotein (HDL) cholesterol (HDL-C). In humans, high amounts of HDL-C in plasma are associated with a lower risk of coronary heart disease (CHD). Mice that have depleted Scarb1 (SR-BI knockout mice) have markedly elevated HDL-C levels but, paradoxically, increased atherosclerosis. The impact of SR-BI on HDL metabolism and CHD risk in humans remains unclear. Through targeted sequencing of coding regions of lipid-modifying genes in 328 individuals with extremely high plasma HDL-C levels, we identified a homozygote for a loss-of-function variant, in which leucine replaces proline 376 (P376L), in SCARB1, the gene encoding SR-BI. The P376L variant impairs posttranslational processing of SR-BI and abrogates selective HDL cholesterol uptake in transfected cells, in hepatocyte-like cells derived from induced pluripotent stem cells from the homozygous subject, and in mice. Large population-based studies revealed that subjects who are heterozygous carriers of the P376L variant have significantly increased levels of plasma HDL-C. P376L carriers have a profound HDL-related phenotype and an increased risk of CHD (odds ratio = 1.79, which is statistically significant).

Introduction: Identifying circulating metabolites that are associated with cognition and dementia may improve our understanding of the pathogenesis of dementia and provide crucial readouts for preventive and therapeutic interventions. Methods: We studied 299 metabolites in relation to cognition (general cognitive ability) in two discovery cohorts (N total = 5658). Metabolites significantly associated with cognition after adjusting for multiple testing were replicated in four independent cohorts (N total = 6652), and the associations with dementia and Alzheimer's disease (N = 25,872) and lifestyle factors (N = 5168) were examined. Results: We discovered and replicated 15 metabolites associated with cognition including subfractions of high-density lipoprotein, docosahexaenoic acid, ornithine, glutamine, and glycoprotein acetyls. These associations were independent of classical risk factors including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, and apolipoprotein E (APOE) genotypes. Six of the cognition-associated metabolites were related to the risk of dementia and lifestyle factors. Discussion: Circulating metabolites were consistently associated with cognition, dementia, and lifestyle factors, opening new avenues for prevention of cognitive decline and dementia. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

11 beta-hydroxysteroid dehydrogenase type 1 (11 beta-HSD-1) activity and mRNA levels are increased in visceral and subcutaneous adipose tissues of metabolic syndrome subjects. We analyzed 11 beta-HSD-1 expression in human epicardial adipose (EA) and ascending aorta (AA) tissues of metabolic syndrome patients and examined their contribution to the development of coronary atherosclerosis. The 11 beta-HSD-1 expression was evaluated by qRT-PCR in EA and AA tissues of 20 metabolic syndrome patients with coronary artery disease (metabolic syndrome group) and 10 non-metabolic syndrome patients without coronary artery disease (controls). 11 beta-HSD-1 expression was increased in EA and AA tissues of the metabolic syndrome group (4.1-and 5.5-fold, respectively). A significant positive correlation was found between 11 beta-HSD-1 expression in EA tissue and waist hip ratio and 11 beta-HSD-1 expression in AA tissue and body mass index, while a negative correlation was found between 11 beta-HSD-1 expression in EA tissue and HDL. Expression of CD68, a macrophage marker, was significantly increased in both tissues of the metabolic syndrome group; it was 2-fold higher in AA tissue compared to EA tissue in the metabolic syndrome group. Our findings of increased expression of 11 beta-HSD-1 and CD68 in AA tissue of the metabolic syndrome group lead us to suggest that they contribute to coronary atherosclerosis in metabolic syndrome. This positive correlation between obesity markers and 11 beta-HSD-1 in AA and EA tissues strengthens the evidence that 11 beta-HSD-1 has a role in metabolic syndrome. To the best of our knowledge, this is the first report showing 11 beta-HSD-1 and CD68 expression in AA tissue of metabolic syndrome patients. We suggest that there is tissue-specific expression of 11 beta-HSD-1 in metabolic syndrome and associated cardiovascular disorders.

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

Polycystic ovary syndrome (PCOS) is a common endocrine condition in women of reproductive age understudied in non-European populations. In India, PCOS affects the life of up to 19.4 million women of age 14-25 years. Gut microbiome composition might contribute to PCOS susceptibility. We profiled the microbiome in DNA isolated from faecal samples by 16S rRNA sequencing in 19/20 women with/without PCOS from Kashmir, India. We assigned genera to sequenced species with an average 121k reads depth and included bacteria detected in at least 1/3 of the subjects or with average relative abundance ≥0.1%. We compared the relative abundances of 40/58 operational taxonomic units in family/genus level between cases and controls, and in relation to 33 hormonal and metabolic factors, by multivariate analyses adjusted for confounders, and corrected for multiple testing. Seven genera were significantly enriched in PCOS cases: Sarcina, Alkalibacterium and Megasphaera, and previously reported for PCOS Bifidobacterium, Collinsella, Paraprevotella and Lactobacillus. We identified significantly increased relative abundance of Bifidobacteriaceae (median 6.07% vs. 2.77%) and Aerococcaceae (0.03% vs. 0.004%), whereas we detected lower relative abundance Peptococcaceae (0.16% vs. 0.25%) in PCOS cases. For the first time, we identified a significant direct association between butyrate producing Eubacterium and follicle-stimulating hormone levels. We observed increased relative abundance of Collinsella and Paraprevotella with higher fasting blood glucose levels, and Paraprevotella and Alkalibacterium with larger hip and waist circumference, and weight. We show a relationship between gut microbiome composition and PCOS linking it to specific reproductive health metabolic and hormonal predictors in Indian women.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

Background. Major depressive disorder (MDD) is moderately heritable, however genome-wide association studies (GWAS) for MDD, as well as for related continuous outcomes, have not shown consistent results. Attempts to elucidate the genetic basis of MDD may be hindered by heterogeneity in diagnosis. The Center for Epidemiological Studies Depression (CES-D) scale provides a widely used tool for measuring depressive symptoms clustered in four different domains which can be combined together into a total score but also can be analysed as separate symptom domains. Method. We performed a meta-analysis of GWAS of the CES-D symptom clusters. We recruited 12 cohorts with the 20-or 10-item CES-D scale (32 528 persons). Results. One single nucleotide polymorphism (SNP), rs713224, located near the brain-expressed melatonin receptor (MTNR1A) gene, was associated with the somatic complaints domain of depression symptoms, with borderline genome-wide significance (p(discovery) = 3.82 x 10(-8)). The SNP was analysed in an additional five cohorts comprising the replication sample (6813 persons). However, the association was not consistent among the replication sample (p(discovery+replication) = 1.10 x 10(-6)) with evidence of heterogeneity. Conclusions. Despite the effort to harmonize the phenotypes across cohorts and participants, our study is still underpowered to detect consistent association for depression, even by means of symptom classification. On the contrary, the SNP-based heritability and co-heritability estimation results suggest that a very minor part of the variation could be captured by GWAS, explaining the reason of sparse findings.

Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers. We included 127 Alzheimer's disease (AD) patients and 121 control subjects with cerebrospinal fluid biomarker-confirmed diagnosis (cutoff tau/amyloid β peptide 42: 0.52). Mass spectrometry platforms determined the concentrations of 53 amine compounds, 22 organic acid compounds, 120 lipid compounds, and 40 oxidative stress compounds. Multiple signatures were assessed: differential expression (nested linear models), classification (logistic regression), and regulatory (network extraction). Twenty-six metabolites were differentially expressed. Metabolites improved the classification performance of clinical variables from 74% to 79%. Network models identified five hubs of metabolic dysregulation: tyrosine, glycylglycine, glutamine, lysophosphatic acid C18:2, and platelet-activating factor C16:0. The metabolite network for apolipoprotein E (APOE) ε4 negative AD patients was less cohesive compared with the network for APOE ε4 positive AD patients. Multiple signatures point to various promising peripheral markers for further validation. The network differences in AD patients according to APOE genotype may reflect different pathways to AD. •Multiple metabolic signatures point to peripheral AD markers for future validation.•AD may be described by changes in the metabolism of amines and oxidative stressors.•APOE ε4-driven AD and non- APOE ε4-driven AD represent different biochemical pathways.•Network analyses of metabolomics data enable the study of metabolic changes in AD.

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P

Elevated serum urate levels can cause gout, an excruciating disease with suboptimal treatment. Previous GWAS identified common variants with modest effects on serum urate. Here we report large-scale whole-exome sequencing association studies of serum urate and kidney function among ≤19,517 European ancestry and African-American individuals. We identify aggregate associations of low-frequency damaging variants in the urate transporters SLC22A12 (URAT1; p  = 1.3 × 10 −56 ) and SLC2A9 ( p  = 4.5 × 10 −7 ). Gout risk in rare SLC22A12 variant carriers is halved (OR = 0.5, p  = 4.9 × 10 −3 ). Selected rare variants in SLC22A12 are validated in transport studies, confirming three as loss-of-function (R325W, R405C, and T467M) and illustrating the therapeutic potential of the new URAT1-blocker lesinurad. In SLC2A9 , mapping of rare variants of large effects onto the predicted protein structure reveals new residues that may affect urate binding. These findings provide new insights into the genetic architecture of serum urate, and highlight molecular targets in SLC22A12 and SLC2A9 for lowering serum urate and preventing gout. Elevated serum urate levels are a risk factor for gout. Here, Tin et al. perform whole-exome sequencing in 19,517 individuals and detect low-frequency genetic variants in urate transporter genes, SLC22A12 and SLC2A9 , associated with serum urate levels and confirm their damaging nature in vitro and in silico.

Background-The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease. We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent coronary heart disease. Methods and Results-We studied a total of 25 109 European ancestry and African ancestry participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52 869 participants with common carotid intima-media thickness measured by ultrasonography within the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology). Participants were genotyped for 247 870 DNA sequence variants (231 539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and carotid intima-media thickness. APOB p.Arg3527Gln was associated with 4-fold excess CAC (P=3x10(-10)). The APOE epsilon 2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P=1x10(-12)) and 1.4% reduced carotid intima-media thickness (P=4x10(-14)) in carriers compared with noncarriers. In secondary analyses conditioning on low-density lipoprotein cholesterol concentration, the epsilon 2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of epsilon 2 was associated with reduced risk for coronary heart disease (odds ratio 0.77; P=1x10(-11)). Conclusions-Exome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE epsilon 2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities, as well as clinical coronary heart disease.

Mendelian randomization (MR) provides us the opportunity to investigate the causal paths of metabolites in type 2 diabetes and glucose homeostasis. We developed and tested an MR approach based on genetic risk scoring for plasma metabolite levels, utilizing a pathway-based sensitivity analysis to control for nonspecific effects. We focused on 124 circulating metabolites that correlate with fasting glucose in the Erasmus Rucphen Family (ERF) study (n = 2,564) and tested the possible causal effect of each metabolite with glucose and type 2 diabetes and vice versa. We detected 14 paths with potential causal effects by MR, following pathway-based sensitivity analysis. Our results suggest that elevated plasma triglycerides might be partially responsible for increased glucose levels and type 2 diabetes risk, which is consistent with previous reports. Additionally, elevated HDL components, i.e., small HDL triglycerides, might have a causal role of elevating glucose levels. In contrast, large (L) and extra large (XL) HDL lipid components, i.e., XL-HDL cholesterol, XL-HDL-free cholesterol, XL-HDL phospholipids, L-HDL cholesterol, and L-HDL-free cholesterol, as well as HDL cholesterol seem to be protective against increasing fasting glucose but not against type 2 diabetes. Finally, we demonstrate that genetic predisposition to type 2 diabetes associates with increased levels of alanine and decreased levels of phosphatidylcholine alkyl-acyl C42:5 and phosphatidylcholine alkyl-acyl C44:4. Our MR results provide novel insight into promising causal paths to and from glucose and type 2 diabetes and underline the value of additional information from high-resolution metabolomics over classic biochemistry.

Electrocardiogram (ECG) measurements play a key role in the diagnosis and prediction of cardiac arrhythmias and sudden cardiac death. ECG parameters, such as the PR, QRS, and QT intervals, are known to be heritable and genome-wide association studies of these phenotypes have been successful in identifying common variants; however, a large proportion of the genetic variability of these traits remains to be elucidated. The aim of this study was to discover loci potentially harboring rare variants utilizing variance component linkage analysis in 1547 individuals from a large family-based study, the Erasmus Rucphen Family Study (ERF). Linked regions were further explored using exome sequencing. Five suggestive linkage peaks were identified: two for QT interval (1q24, LOD = 2.63; 2q34, LOD = 2.05), one for QRS interval (1p35, LOD = 2.52) and two for PR interval (9p22, LOD = 2.20; 14q11, LOD = 2.29). Fine-mapping using exome sequence data identified a C > G missense variant (c.713C > G, p.Ser238Cys) in the FCRL2 gene associated with QT (rs74608430; P = 2.8 x 10(-4), minor allele frequency = 0.019). Heritability analysis demonstrated that the SNP explained 2.42% of the trait's genetic variability in ERF (P = 0.02). Pathway analysis suggested that the gene is involved in cytosolic Ca2+ levels (P = 3.3 x 10(-3)) and AMPK stimulated fatty acid oxidation in muscle (P = 4.1 x 10(-3)). Look-ups in bioinformatics resources showed that expression of FCRL2 is associated with ARHGAP24 and SETBP1 expression. This finding was not replicated in the Rotterdam study. Combining the bioinformatics information with the association and linkage analyses, FCRL2 emerges as a strong candidate gene for QT interval.

Background Although the vertical cup-disc ratio (VCDR) and intraocular pressure (IOP) are important determinants of open angle glaucoma (OAG), it is unclear to what extent the genetic origin of these traits overlap with those of OAG. We evaluated whether the same genes that determine VCDR and IOP also predict OAG. Methods Genetic risk scores were constructed from single nucleotide polymorphisms (SNPs) using genome wide association data of 9326 participants from the Rotterdam Study cohorts (mean +/- SD age: 64.6 +/- 9.1 years). These risk scores were used to calculate the explained variance of VCDR and IOP in an independent cohort (Erasmus Rucphen Family study) consisting of 1646 participants (mean +/- SD age: 46.8 +/- 14.1 years) and the OAG risk in a subset of the Rotterdam Study cohorts. To evaluate false positive findings, we generated two new variables containing randomly sampled values to serve as a negative control. Results The explained variance of VCDR increased when increasing the number of SNPs included in the risk score, suggesting a polygenic model. We found no clear evidence for a similar model for IOP, suggesting that a small number of SNPs determine the susceptibility to IOP. The SNPs related to IOP in terms of p values contributed little to VCDR. The risk scores associated with VCDR were also associated significantly with OAG. This suggests a common polygenic background for VCDR and OAG Conclusions We found evidence for a polygenic model underlying one of the major traits of OAG, VCDR, and OAG itself. The IOP did not show any evidence for such a model.

The central nervous system has the second highest concentration of lipids after adipose tissue. Alterations in neural membrane phospho- and sphingolipid composition can influence crucial intra- and intercellular signalling and alter the membrane's properties. Recently, the polyunsaturated fatty acids (PUFA) hypothesis for depression suggests that phospho- and sphingolipid metabolism includes potential pathways for the disease. In 742 people from a Dutch family-based study, we assessed the relationships between 148 different plasma phospho- and sphingolipid species and depression/anxiety symptoms as measured by the Hospital Anxiety and Depression Scales (HADS-A and HADS-D) and the Centre for Epidemiological Studies Depression Scale (CES-D). We observed significant differences in plasma sphingomyelins (SPM), particularly the SPM 23:1/SPM 16:0 ratio, which was inversely correlated with depressive symptom scores. We observed a similar trend for plasma phosphatidylcholines (PC), particularly the molar proportion of PC O 36:4 and its ratio to ceramide CER 20:0. Absolute levels of PC O 36:4 were also associated with depression symptoms in an independent replication. To our knowledge this is the first study on depressive symptoms that focuses on specific phospho- and sphingolipid molecules in plasma rather than total PUFA concentrations. The findings of this lipidomic study suggests that plasma sphingomyelins and ether phospholipids should be further studied for their potential as biomarkers and for a better understanding of the underlying mechanisms of this systemic disease. (C) 2012 Published by Elsevier Ltd.

Aims: Glutathione S-transferase P1 (GSTP1) plays an important role in cellular protection against oxidative stress and toxic chemicals. Polymorphisms within GSTP1 are associated with alterations in enzyme activity, which may lead to development of lung disease and cancer. In this study, we aimed to investigate the GSTP1 Ile105Val and Ala114Val polymorphisms in patients with small cell lung cancer (SCLC). Patients/Methods: GSTP1 Ile105Val polymorphism in exon 5 and GSTP1 Ala114Val polymorphism in exon 6 were determined by using polymerase chain reaction-restriction fragment length polymorphism techniques in 89 patients with SCLC and 108 control patients with chronic obstructive pulmonary disease (COPD). Genotype frequencies and cigarette smoking intensities were compared among SCLC and COPD patients. Results: There were significantly less SCLC patients with variant exon 6 genotypes than COPD patients (7.9% vs. 20.4%, p = 0.007), while the number of patients with variant exon 5 genotypes were comparable among groups. SCLC and COPD patients with variant exon 6 genotype showed trends toward exhibiting reduced cigarette consumption. Conclusions: The variant GSTP1 exon 6 genotype might be conferring protection against SCLC development. Whether this effect is associated with exposure to cigarette smoking needs to be clarified.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 x 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Bedside lung ultrasonography (LUS) is a reliable method for the diagnosis of pneumonia. No common consensus exists in the literature regarding the effectiveness of LUS findings, except consolidation, for the diagnosis of pneumonia. The primary objective of this study was to investigate the effectiveness of LUS for the diagnosis of pneumonia, and the secondary objective was to investigate the use of LUS findings, except consolidation, for the diagnosis of pneumonia. A total of 127 patients with clinically suspected pneumonia were evaluated in the study. The sensitivity and specificity of LUS were 98.0% and 95.8%, respectively. In the cases where consolidation was not determined in LUS but B-3 line positivity or a diffuse interstitial pattern was present, the sensitivity and specificity were 93.3% and 88.2%, respectively. When consolidation was not observed in LUS, the presence of B-3 line positivity or diffuse interstitial pattern could be used for the diagnosis of pneumonia.

Primary open-angle glaucoma is the most common optic neuropathy and an important cause of irreversible blindness worldwide. The optic nerve head or optic disc is divided in two parts: a central cup (without nerve fibers) surrounded by the neuroretinal rim (containing axons of the retinal ganglion cells). The International Glaucoma Genetics Consortium conducted a meta-analysis of genome-wide association studies consisting of 17,248 individuals of European ancestry and 6,841 individuals of Asian ancestry. The outcomes of the genome-wide association studies were disc area and cup area. These specific measurements describe optic nerve morphology in another way than the vertical cup-disc ratio, which is a clinically used measurement, and may shed light on new glaucoma mechanisms. We identified 10 new loci associated with disc area (CDC42BPA, F5, DIRC3, RARB, ABI3BP, DCAF4L2, ELP4, TMTC2, NR2F2, and HORMAD2) and another 10 new loci associated with cup area (DHRS3, TRIB2, EFEMP1, FLNB, FAM101, DDHD1, ASB7, KPNB1, BCAS3, and TRIOBP). The new genes participate in a number of pathways and future work is likely to identify more functions related to the pathogenesis of glaucoma.

Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.

A new capillary isotachophoresis (cITP) method for lipoprotein profiling with superior lipoprotein coverage compared to previous methods has been developed, resolving twice as many lipoprotein species (18 peaks/fractions) in serum or plasma in less than 9.5 min. For this, a novel mixture of 24 spacers, including amino acids, dipeptides and sulfonic acids, was developed and fine-tuned, using predictive software (PeakMaster) and testing of spiked serum samples. Lipoprotein peaks were identified by serum-spiking with reference lipoproteins. Compatibility with common lipophilic stains for selective lipoprotein detection with either UV/Vis or laser-induced fluorescence was demonstrated. A special new capillary with a neutral coating (combining water-compatible OV1701-OH deactivation and methylation) was used for the first time for electrodriven separations, allowing very stable separations in a pH 8.8-9.4 gradient system, being functional for more than 100 injections. Excellent reproducibility was achieved, with coefficients of variation lower than 2.6% for absolute migration times. Comparison was performed with human plasma samples analyzed by NMR, leading to similar results with cITP after multivariate statistics, regarding group-clustering and lipoprotein species correlation. The new cITP method was applied to the analysis of serum samples from a LDL receptor knock-out mice model fed either a normal diet or a western-type diet. Differences in the lipoprotein levels and in the sublipoprotein types were detected, showing a shift to more atherogenic particles due to the high cholesterol diet. In summary, this novel method will allow more detailed and informative profiling of lipoprotein particle subtypes for cardiovascular disease research. (C) 2016 Elsevier B.V. All rights reserved.

Metabolite, lipid, and lipoprotein lipid profiling can provide novel insights into mechanisms underlying incident dementia and Alzheimer's disease. We studied eight prospective cohorts with 22,623 participants profiled by nuclear magnetic resonance or mass spectrometry metabolomics. Four cohorts were used for discovery with replication undertaken in the other four to avoid false positives. For metabolites that survived replication, combined association results are presented. Over 246,698 person-years, 995 and 745 cases of incident dementia and Alzheimer's disease were detected, respectively. Three branched-chain amino acids (isoleucine, leucine, and valine), creatinine and two very low density lipoprotein (VLDL)-specific lipoprotein lipid subclasses were associated with lower dementia risk. One high density lipoprotein (HDL; the concentration of cholesterol esters relative to total lipids in large HDL) and one VLDL (total cholesterol to total lipids ratio in very large VLDL) lipoprotein lipid subclass was associated with increased dementia risk. Branched-chain amino acids were also associated with decreased Alzheimer's disease risk and the concentration of cholesterol esters relative to total lipids in large HDL with increased Alzheimer's disease risk. Further studies can clarify whether these molecules play a causal role in dementia pathogenesis or are merely markers of early pathology.

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P

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 x 10(-6)), methionine (p-value=9.2 x 10(-5)), tyrosine (p-value=2.1 x 10(-4)), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value=2.4 x 10(-4)), hydroxypropionylcarnitine (C3-OH, p-value=2.6 x 10(-4)), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value=9.0 x 10(-4)). 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.

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic β-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p -values ranging down to 9.08×10 −66 . The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3 , LASS4 , SGPP1 , ATP10D , and FADS1–3 . Variants in 3 loci ( ATP10D , FADS3 , and SPTLC3 ) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association ( p  = 10 −4 or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases. Although several rare monogenic diseases are caused by defects in enzymes involved in sphingolipid biosynthesis and metabolism, little is known about the major variants that control the circulating levels of these important bioactive molecules. As well as being essential components of plasma membranes and endosomes, sphingolipids play critical roles in cell surface protection, protein and lipid transport and sorting, and cellular signalling cascades. Experimental evidence supports a role for sphingolipids in several common complex chronic metabolic, cardiovascular, or neurological disease processes. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, and discovering the genetic variants that influence their circulating concentrations is an important step towards understanding how the genetic control of sphingolipids might contribute to common human disease. We have identified 32 variants in 7 genes that have a strong effect on the circulating plasma levels of 33 distinct sphingolipids, and 43 matched metabolite ratios. In a series of 3 German MI studies, we see association with MI for variants in 3 of the genes tested. Further cardiovascular, metabolic, neurological, and psychiatric disease associations can be tested with the variants described here, which may identify additional disease risk and potentially useful therapeutic targets.

Time to fall asleep (sleep latency) is a major determinant of sleep quality. Chronic, long sleep latency is a major characteristic of sleep-onset insomnia and/or delayed sleep phase syndrome. In this study we aimed to discover common polymorphisms that contribute to the genetics of sleep latency. We performed a meta-analysis of genome-wide association studies (GWAS) including 2 572 737 single nucleotide polymorphisms (SNPs) established in seven European cohorts including 4242 individuals. We found a cluster of three highly correlated variants (rs9900428, rs9907432 and rs7211029) in the RNA-binding protein fox-1 homolog 3 gene (RBFOX3) associated with sleep latency (P-values = 5.77 x 10(-08), 6.59 x 10(-08) and 9.17 x 10(-08)). These SNPs were replicated in up to 12 independent populations including 30 377 individuals (P-values = 1.5 x 10(-02), 7.0 x 10(-03) and 2.5 x 10(-03); combined meta-analysis P-values = 5.5 x 10(-07), 5.4 x 10(-07) and 1.0 x 10(-07)). A functional prediction of RBFOX3 based on co-expression with other genes shows that this gene is predominantly expressed in brain (P-value = 1.4 x 10(-316)) and the central nervous system (P-value = 7.5 x 10(-321)). The predicted function of RBFOX3 based on co-expression analysis with other genes shows that this gene is significantly involved in the release cycle of neurotransmitters including gamma-aminobutyric acid and various monoamines (P-values

Background and aims: Increasing evidence shows that intracranial carotid artery atherosclerosis may develop under the influence of a differential metabolic risk factor profile than atherosclerosis in the extracranial part of the carotid artery. To further elucidate these differences, we investigated associations of a wide range of circulating metabolites with intracranial and extracranial carotid artery atherosclerosis.& para;& para;Methods: From the population-based Rotterdam Study, blood samples from 1111 participants were used to determine a wide range of metabolites by proton nuclear magnetic resonance (NMR). Moreover, these participants underwent non-contrast computed tomography of the neck and head to quantify the amount of extra-and intracranial carotid artery calcification (ECAC and ICAC), as a proxy of atherosclerosis. We assessed associations of the metabolites with ICAC and ECAC and compared the metabolic association patterns of the two.& para;& para;Results: We found that one standard deviation (SD) increase in concentration of 3-hydroxybutyrate, a ketone body, was significantly associated with a 0.11 SD increase in ICAC volume (p = 1.8 x 10(-4)). When we compared the metabolic association pattern of ICAC with that of ECAC, we observed differences in glycolysis-related metabolite measures, lipoprotein subfractions, and amino acids. Interestingly, glyco-protein acetyls were associated with calcification in both studied vessel beds. These associations were most prominent in men.& para;& para;Conclusions: We found that a higher circulating level of 3-hydroxybutyrate was associated with an increase in ICAC. Furthermore, we found differences in metabolic association patterns of ICAC and ECAC, providing further evidence for location-specific differences in the etiology of atherosclerosis. (C) 2018 Elsevier B.V. All rights reserved.

To identify a plasma metabolomic biomarker signature for migraine. Plasma samples from 8 Dutch cohorts (n = 10,153: 2,800 migraine patients and 7,353 controls) were profiled on a H-NMR-based metabolomics platform, to quantify 146 individual metabolites (e.g., lipids, fatty acids, and lipoproteins) and 79 metabolite ratios. Metabolite measures associated with migraine were obtained after single-metabolite logistic regression combined with a random-effects meta-analysis performed in a nonstratified and sex-stratified manner. Next, a global test analysis was performed to identify sets of related metabolites associated with migraine. The Holm procedure was applied to control the family-wise error rate at 5% in single-metabolite and global test analyses. Decreases in the level of apolipoprotein A1 (β -0.10; 95% confidence interval [CI] -0.16, -0.05; adjusted = 0.029) and free cholesterol to total lipid ratio present in small high-density lipoprotein subspecies (HDL) (β -0.10; 95% CI -0.15, -0.05; adjusted = 0.029) were associated with migraine status. In addition, only in male participants, a decreased level of omega-3 fatty acids (β -0.24; 95% CI -0.36, -0.12; adjusted = 0.033) was associated with migraine. Global test analysis further supported that HDL traits (but not other lipoproteins) were associated with migraine status. Metabolic profiling of plasma yielded alterations in HDL metabolism in migraine patients and decreased omega-3 fatty acids only in male migraineurs.

Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88 x 10(-204)) and 10 loci for sphingolipids (smallest P-value = 3.10 x 10(-57)). After a correction for multiple comparisons (P-value, 2.2 x 10(-9)), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.

[This corrects the article DOI: 10.1371/journal.pgen.1006528.].

[This corrects the article DOI: 10.1371/journal.pgen.1006528.].

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P 

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.

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P 5.6 10(9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 10(4)2.2 10(7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.

Background The growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays. Objectives We aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF). Methods We analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up. Results The 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age < 50 years) (0.86 vs. 0.77, p-value

Hypertension is an important determinant of cardiovascular morbidity and mortality and has a substantial heritability, which is likely of polygenic origin. The aim of this study was to assess to what extent multiple common genetic variants contribute to blood pressure regulation in both adults and children and to assess overlap in variants between different age groups, using genome-wide profiling. Single nucleotide polymorphism sets were defined based on a meta-analysis of genome-wide association studies on systolic blood pressure and diastolicblood pressure performed by the Cohort for Heart and Aging Research in Genome Epidemiology (n = 29 136), using different P value thresholds for selecting single nucleotide polymorphisms. Subsequently, genetic risk scores for systolic blood pressure and diastolic blood pressure were calculated in an independent adult population (n = 2072) and a child population (n = 1034). The explained variance of the genetic risk scores was evaluated using linear regression models, including sex, age, and body mass index. Genetic risk scores, including also many nongenome-wide significant single nucleotide polymorphisms, explained more of the variance than scores based only on very significant single nucleotide polymorphisms in adults and children. Genetic risk scores significantly explained

Blood pressure is a heritable trait1 influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or ≥90 mm Hg diastolic blood pressure)2. Even small increments in blood pressure are associated with an increased risk of cardiovascular events3. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention

Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P

Metabolic syndrome is a cluster of different clinical manifestations that are risk factors for atherothrombotic cardiovascular disorders. Fatty-acid-binding protein 4 (FABP4/aP2), which is highly expressed in adipocytes, specifically exerts intracellular lipid trafficking. A high level of fatty-acid-binding protein 4 expression present in obese subjects has also been found in mice and humans, especially in macrophages at atherosclerotic lesions. An in vivo study demonstrated that the inhibitor of aP2 would be a new therapeutic agent for treating metabolic diseases in mice. We have investigated the mRNA expression of fatty-acid-binding protein 4 in human epicardial adipose and ascending aorta tissues of metabolic syndrome and nonmetabolic syndrome patients. Paired epicardial adipose and ascending aorta tissue samples were obtained from 10 metabolic syndrome patients and 4 nonmetabolic syndrome patients during coronary bypass grafting and aortic valve replacement therapy, respectively. Fatty-acid-binding protein 4 gene expression was determined by quantitative real-time polymerase chain reaction. Fatty-acid-binding protein 4 expression of epicardial adipose tissue was significantly higher in metabolic syndrome patients than in nonmetabolic syndrome controls ( P

Introduction: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Methods: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. Results: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and alpha-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for A beta(1-42), tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. Discussion: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. (C) 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Aim: Preeclampsia (PE) is one of the most serious disorders of pregnancy. The imbalance between pro- and anti-inflammatory cytokines may play a role in its etiology. The aim of the present study was to investigate whether cytokine gene polymorphism is associated with PE, and to evaluate the relationship between genotypes and clinical/laboratory manifestation of PE. Methods: We investigated single nucleotide polymorphisms of tumor necrosis factor (TNF)alpha(-308 G/A), interleukin (IL)-6 (-174 G/C), IL-10 (-1082 G/A) genes in DNA from peripheral blood leukocytes of 101 PE patients and 95 healthy control women. Results: In PE, there was a significant increase of the IL-10 (-1082) A allele frequency (P = 0.04). No significant differences were found in genotypes or allele frequencies of TNF alpha(-308) and IL-6 (-174) genes between PE women and controls. While TNF alpha(-308) and IL-6 (-174) genotypes did not influence clinical/laboratory parameters in PE, IL-10 (-1082) A allele carrying genotypes (AG + AA) were associated with higher glucose and lower HDL-cholesterol levels. Conclusion: Because women with IL-10 (-1082) AA genotype have 3.38-fold increased risk of developing PE according to GG genotype (95% CI 1.21-9.4, P = 0.01), we suggest that IL-10 (-1082) variant A allele is associated with an increased risk of preeclampsia, which is independent from its metabolic effects.

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant ( Z -test, P

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 ( P = 3.6 × 10 −8 ) located near GPR98 and ARRDC3 . For SBP, the strongest signal was rs2258119 in C21orf91 ( P = 4.7 × 10 −8 ). The top IBC association for SBP was rs2012318 ( P = 6.4 × 10 −6 ) near SLC25A42 and for DBP was rs2523586 ( P = 1.3 × 10 −6 ) near HLA-B . None of the top variants replicated in additional AA ( n = 11 882) or European-American ( n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples ( SH2B3 , P = 0.009; TBX3-TBX5 , P = 0.03; and CSK-ULK3 , P = 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity.

Purpose: Many of commonly used chemotherapeutics in lung cancer treatment are metabolized by glutathione-S transferases (GSTs). The placental isoform of GST (GSTP1) is the most abundant isoform in the lung. Polymorphisms within the GSTP1 may result in alterations in enzyme activity and change sensitivity to platinum-based chemotherapy. We investigated whether the polymorphism within the exons 5 and 6 of GSTP1 gene may change response to therapy, time to tumor progression (TTP) and overall survival in small cell lung cancer (SCLC) patients. Methods: Ninety-four histologically confirmed patients with SCLC were enrolled in this study during 1995-2006. GSTP1 Ile105Val polymorphism in exon 5 and GSTP1 Ala114Val polymorphism in exon 6 were determined by using PCR-RFLP techniques. Associations between the GSTP1 polymorphisms and treatment response were evaluated using the chi-square test. Associations between the GSTP1 polymorphisms and TTP and overall survival were compared using Kaplan-Meier survival curves. Results: We found no significant associations between exon 5 and exon 6 GSTP1 gene polymorphisms and response to therapy or overall survival. Patients carrying both variant exon 5 (Ile/Val or Val/Val) and variant exon 6 (Ala/Val) genotypes had significantly shorter TTP (5 vs. 8 months, p = 0.04). Moreover, patients with heterozygote exon 6 variant had presented with extensive-stage disease. Conclusion: No individual effect of variant alleles was found in relation to chemotherapy response, median TTP and overall survival. The carriage of both types of variant alleles may predict worse outcome.

Introduction Polygenic Score (PGS) is a valuable method for assessing the estimated genetic liability to a given outcome or genetic variability contributing to a quantitative trait. While PRSs are widely used for complex traits, their application in uncovering shared genetic predisposition between phenotypes, i.e. when genetic variants influence more than one phenotype, remains limited. Methods We developed an R package, comorbidPGS, which facilitates a systematic evaluation of shared genetic effects among (cor)related phenotypes using PGSs. The comorbidPGS package takes as input a set of Single Nucleotide Polymorphisms (SNPs) along with their established effects on the original phenotype (Po), referred to as Po-PGS. It generates a comprehensive summary of effect(s) of Po-PGS on target phenotype(s) (Pt) with customisable graphical features. Results We applied comorbidPGS to investigate the shared genetic predisposition between phenotypes defining elevated blood pressure (Systolic Blood Pressure, SBP; Diastolic Blood Pressure, DBP; Pulse Pressure, PP) and several cancers (Breast Cancer, BrC; Pancreatic Cancer, PanC; Kidney Cancer, KidC; Prostate Cancer, PrC; Colorectal Cancer, CrC) using the European ancestry UK Biobank individuals and GWAS meta-analyses summary statistics from independent set of European ancestry individuals. We report a significant association between elevated DBP and the genetic risk of PrC (β (SE)=0.066 (0.017), P-value=9.64×10^(-5)), as well as between CrC PGS and both, lower SBP (β (SE)=-0.10 [0.029], P-value=3.83×10^(-4))) and lower DBP (β (SE)=-0.055 [0.017], P-value=1.05×10^(-3)). Our analysis highlights two nominally significant relationships for individuals with genetic predisposition to elevated SBP leading to higher risk of KidC (OR [95%CI]=1.04 [1.0039-1.087], P-value=2.82×10^(-2)) and PrC (OR [95%CI]=1.02 [1.003-1.041], P-value=2.22×10^(-2)). Conclusion Using comorbidPGS, we underscore mechanistic relationships between blood pressure regulation and susceptibility to three comorbid malignancies. This package offers valuable means to evaluate shared genetic susceptibility between (cor)related phenotypes through polygenic scores.

Background: The intra-tumoural microbiome can influence pancreatic tumourigenesis and chemoresistance, and therefore patient survival. The role played by bile microbiota in PDAC is unknown. We aimed to define bile microbiome signatures in patients presenting with obstructive jaundice caused by benign and malignant pancreaticobiliary disease to develop novel cancer biomarkers. Methods: Prospective bile samples were obtained from 37 patients who underwent either endoscopic retrograde cholangiopancreatography (ERCP) or percutaneous transhepatic cholangiography (PTC). Variable regions (V3–V4) of the 16S rRNA genes were amplified by PCR and next generation sequencing was performed. The cohort consisted of 12 PDAC, 6 cholangiocarcinoma, 10 choledocholithiasis, 7 gallstone pancreatitis and 2 primary sclerosing cholangitis patients. Bile samples from 8 patients were excluded from the analysis because of low read count. Results: Using the 16S rRNA method, we identified a total of 108 genera from 29 individuals (12 PDAC and 17 benign). Bile microbial diversity significantly differed between patients with PDAC vs. benign disease (p=0.0173). The separation of PDAC from benign samples is clearly seen through unsupervised clustering based on Canberra distances. We found 4 genera to be of significantly different abundance between PDAC vs. benign groups by association p-value and supported by false discovery rate (fdr). These were Escherichia, Rothia, Streptococcus and Prevotella. Conclusion: We show that patients with obstructive jaundice caused by PDAC have an altered microbiome composition in the bile, compared to those with benign disease. These bile-based microbes could be developed into potential diagnostic and prognostic biomarkers for PDAC and warrant further investigation.

Polycystic ovary syndrome (PCOS) is a very common endocrine condition in women in India. Gut microbiome alterations were shown to be involved in PCOS, yet it is remarkably understudied in Indian women who have a higher incidence of PCOS as compared to other ethnic populations. During the regional PCOS screening program among young women, we recruited 19 drug naive women with PCOS and 20 control women at the Sher-i-Kashmir Institute of Medical Sciences, Kashmir, North India. We profiled the gut microbiome in faecal samples by 16S rRNA sequencing and included 40/58 operational taxonomic units (OTUs) detected in at least 1/3 of the subjects with relative abundance (RA) ≥ 0.1%. We compared the RAs at a family/genus level in PCOS/non-PCOS groups and their correlation with 33 metabolic and hormonal factors, and corrected for multiple testing, while taking the variation in day of menstrual cycle at sample collection, age and BMI into account. Five genera were significantly enriched in PCOS cases: , , and previously reported for PCOS , and confirmed by different statistical models. At the family level, the relative abundance of was enriched, whereas was decreased among cases. We observed increased relative abundance of and with higher fasting blood glucose levels, and and with larger hip, waist circumference, weight, and with lower prolactin levels. We also detected a novel association between and follicle-stimulating hormone levels and between and alkaline phosphatase, independently of the BMI of the participants. Our report supports that there is a relationship between gut microbiome composition and PCOS with links to specific reproductive health metabolic and hormonal predictors in Indian women.

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Many Alzheimer's disease (AD) genes including Apolipoprotein E (APOE) are found to be expressed in blood-derived macrophages and thus may alter blood protein levels. We measured 91 neuro-proteins in plasma from 316 participants of the Rotterdam Study (incident AD = 161) using Proximity Extension Ligation assay. We studied the association of plasma proteins with AD in the overall sample and stratified by APOE. Findings from the Rotterdam study were replicated in 186 AD patients of the BioFINDER study. We further evaluated the correlation of these protein biomarkers with total tau (t-tau), phosphorylated tau (p-tau) and amyloid-beta (Aβ) 42 levels in cerebrospinal fluid (CSF) in the Amsterdam Dementia Cohort (N = 441). Finally, we conducted a genome-wide association study (GWAS) to identify the genetic variants determining the blood levels of AD-associated proteins. Plasma levels of the proteins, CDH6 (β = 0.638, P = 3.33 × 10 ) and HAGH (β = 0.481, P = 7.20 × 10 ), were significantly elevated in APOE ε4 carrier AD patients. The findings in the Rotterdam Study were replicated in the BioFINDER study for both CDH6 (β = 1.365, P = 3.97 × 10 ) and HAGH proteins (β = 0.506, P = 9.31 × 10 ) when comparing cases and controls in APOE ε4 carriers. In the CSF, CDH6 levels were positively correlated with t-tau and p-tau in the total sample as well as in APOE ε4 stratum (P 

Aims/hypothesis Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes.Methods One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals.Results Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAF(Afr)]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9x10(-9)); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAF(Afr)=0.0536), was associated with a reduced response to metformin (p=2.4x10(-8)), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increase in HbA(1c) of 0.08% and non-carriers had an HbA(1c) increase of 0.01% after 1 year of treatment (p=3.3x10(-3)). We also identified associations between type 2 diabetes-associated variants and glycaemic response, including the type 2 diabetes-protective C allele of rs703972 near ZMIZ1 and increased levels of active glucagon-like peptide 1 (GLP-1) (p=1.6x10(-5)), supporting the role of alterations in incretin levels in type 2 diabetes pathophysiology.Conclusions/interpretation We present a well-phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common glucose-lowering medications and provide insight into mechanisms of action of type 2 diabetes-related variation.

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p< 1x10(-7) (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.

Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h ), and the proportion of heritability captured by known metabolite loci (h ) for 309 lipids and 52 organic acids. Our study reveals significant differences in h among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 x 10(-8)) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 x 10(-20)), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 x 10(-10) < P < 5 x 10(-8)) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Parkinson's disease (PD) may start in the gut and spread to the brain. To investigate the role of gut microbiome, we conducted a large-scale study, at high taxonomic resolution, using uniform standardized methods from start to end. We enrolled 490 PD and 234 control individuals, conducted deep shotgun sequencing of fecal DNA, followed by metagenome-wide association studies requiring significance by two methods (ANCOM-BC and MaAsLin2) to declare disease association, network analysis to identify polymicrobial clusters, and functional profiling. Here we show that over 30% of species, genes and pathways tested have altered abundances in PD, depicting a widespread dysbiosis. PD-associated species form polymicrobial clusters that grow or shrink together, and some compete. PD microbiome is disease permissive, evidenced by overabundance of pathogens and immunogenic components, dysregulated neuroactive signaling, preponderance of molecules that induce alpha-synuclein pathology, and over-production of toxicants; with the reduction in anti-inflammatory and neuroprotective factors limiting the capacity to recover. We validate, in human PD, findings that were observed in experimental models; reconcile and resolve human PD microbiome literature; and provide a broad foundation with a wealth of concrete testable hypotheses to discern the role of the gut microbiome in PD.

Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Leptin influences food intake by informing the brain about the status of body fat stores. Rare mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in , , , and , and one intergenic variant near The missense variant Val94Met (rs17151919) in was common in individuals of African ancestry only, and its association with lower leptin concentrations was specific to this ancestry ( = 2 × 10 , = 3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting that leptin regulates early adiposity.

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification. Genome-wide association analyses of blood glucose measurements under nonstandardized conditions provide insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P 

Background Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways. Methods To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses. Results Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology. Conclusions Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.

High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology. In this study, we present a multi-layer framework to combine the largest multi-ancestry GWAS to date on lipid levels with both transcriptomic and epigenomic datasets to prioritize regulatory variants, effector genes, cell types, and tissues with strong functional relevance to lipid biology.

One of the goals of personalised medicine (PM) is to use the ever-growing understanding of biology to provide a higher level of precision in disease prevention and patient care. PM strategies include the use of decision-making processes based on biomarker-driven approaches. Genes, gene expression products (i.e. transcripts and proteins) and metabolites are the main biomarker families. Given this molecular diversity of biomarkers, the increase in high-throughput omics technologies offers an amazing opportunity to capture the whole picture of biological systems in a hypothesis-free and unbiased model. This chapter examines as the high-throughput era in omics is progressing and as genomics and other omics will be effective in disentangling the aetiology and progression of the diseases.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P 

Pancreatic ductal adenocarcinoma (PDAC) has a very poor survival. The intra-tumoural microbiome can influence pancreatic tumourigenesis and chemoresistance and, therefore, patient survival. The role played by bile microbiota in PDAC is unknown. We aimed to define bile microbiome signatures that can effectively distinguish malignant from benign tumours in patients presenting with obstructive jaundice caused by benign and malignant pancreaticobiliary disease. Prospective bile samples were obtained from 31 patients who underwent either Endoscopic Retrograde Cholangiopancreatography (ERCP) or Percutaneous Transhepatic Cholangiogram (PTC). Variable regions (V3–V4) of the 16S rRNA genes of microorganisms present in the samples were amplified by Polymerase Chain Reaction (PCR) and sequenced. The cohort consisted of 12 PDAC, 10 choledocholithiasis, seven gallstone pancreatitis and two primary sclerosing cholangitis patients. Using the 16S rRNA method, we identified a total of 135 genera from 29 individuals (12 PDAC and 17 benign). The bile microbial beta diversity significantly differed between patients with PDAC vs. benign disease (Permanova p = 0.0173). The separation of PDAC from benign samples is clearly seen through unsupervised clustering of Aitchison distance. We found three genera to be of significantly lower abundance among PDAC samples vs. benign, adjusting for false discovery rate (FDR). These were Escherichia (FDR = 0.002) and two unclassified genera, one from Proteobacteria (FDR = 0.002) and one from Enterobacteriaceae (FDR = 0.011). In the same samples, the genus Streptococcus (FDR = 0.033) was found to be of increased abundance in the PDAC group. We show that patients with obstructive jaundice caused by PDAC have an altered microbiome composition in the bile compared to those with benign disease. These bile-based microbes could be developed into potential diagnostic and prognostic biomarkers for PDAC and warrant further investigation.

Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons. Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses. Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q < .05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein A1 were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms. This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity.