Author Correction: Exome sequencing of Finnish isolates enhances rare-variant association power.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences.

BACKGROUND: Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). RESULTS: We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10-8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10-21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. CONCLUSION: These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Polygenic Risk Scores in Predicting Coronary Artery Disease in Symptomatic Patients. A Validation Study.

AIM: Clinical risk scores for coronary artery disease (CAD) are used in clinical practice to select patients for diagnostic testing and therapy. Several studies have proposed that polygenic risk scores (PRSs) can improve the prediction of CAD, but the scores need to be validated in clinical populations with accurately characterized phenotypes. We assessed the predictive power of the three most promising PRSs for the prediction of coronary atherosclerosis and obstructive CAD. METHODS: This study was conducted on 943 symptomatic patients with suspected CAD for whom the phenotype was accurately characterized using anatomic and functional imaging. Previously published genome-wide polygenic scores were generated to compare a genetic model based on PRSs with a model based on clinical data. The test and PRS cohorts were predominantly Caucasian of northern European ancestry. RESULTS: All three PRSs predicted coronary atherosclerosis and obstructive CAD statistically significantly. The predictive accuracy of the models combining clinical data and different PRSs varied between 0.778 and 0.805 in terms of the area under the receiver operating characteristic (AUROC), being close to the model including only clinical variables (AUROC 0.769). The difference between the clinical model and combined clinical + PRS model was not significant for PRS1 (p=0.627) and PRS3 (p=0.061). Only PRS2 slightly improved the predictive power of the model (p=0.04). The likelihood ratios showed the very weak diagnostic power of all PRSs. CONCLUSION: The addition of PRSs to conventional risk factors did not clinically significantly improve the predictive accuracy for either coronary atherosclerosis or obstructive CAD, showing that current PRSs are not justified for routine clinical use in CAD.

Plasma Metabolite Signatures in Male Carriers of Genetic Variants Associated with Non-Alcoholic Fatty Liver Disease.

Both genetic and non-genetic factors are important in the pathophysiology of non-alcoholic fatty liver disease (NAFLD). The aim of our study was to identify novel metabolites and pathways associated with NAFLD by including both genetic and non-genetic factors in statistical analyses. We genotyped six genetic variants in the PNPLA3, TM6SF2, MBOAT7, GCKR, PPP1R3B, and HSD17B13 genes reported to be associated with NAFLD. Non-targeted metabolomic profiling was performed from plasma samples. We applied a previously validated fatty liver index to identify participants with NAFLD. First, we associated the six genetic variants with 1098 metabolites in 2 339 men without NAFLD to determine the effects of the genetic variants on metabolites, and then in 2 535 men with NAFLD to determine the joint effects of genetic variants and non-genetic factors on metabolites. We identified several novel metabolites and metabolic pathways, especially for PNPLA3, GCKR, and PPP1R38 variants relevant to the pathophysiology of NAFLD. Importantly, we showed that each genetic variant for NAFLD had a specific metabolite signature. The plasma metabolite signature was unique for each genetic variant, suggesting that several metabolites and different pathways are involved in the risk of NAFLD. The FLI index reliably identifies metabolites for NAFLD in large population-based studies.

Metabolites as Risk Factors for Diabetic Retinopathy in Patients With Type 2 Diabetes: A 12-Year Follow-up Study.

CONTEXT: Diabetic retinopathy (DR) is a specific microvascular complication in patients with diabetes and the leading cause of blindness. Recent advances in omics, especially metabolomics, offer the possibility identifying novel potential biomarkers for DR. OBJECTIVE: The aim was to identify metabolites associated with DR. METHODS: We performed a 12-year follow-up study including 1349 participants with type 2 diabetes (1021 without DR, 328 with DR) selected from the METSIM cohort. Individuals who had retinopathy before the baseline study were excluded (n = 63). The diagnosis of retinopathy was based on fundus photography examination. We performed nontargeted metabolomics profiling to identify metabolites. RESULTS: We found 17 metabolites significantly associated with incident DR after adjustment for confounding factors. Among amino acids, N-lactoyl isoleucine, N-lactoyl valine, N-lactoyl tyrosine, N-lactoyl phenylalanine, N-(2-furoyl) glycine, and 5-hydroxylysine were associated with an increased risk of DR, and citrulline with a decreased risk of DR. Among the fatty acids N,N,N-trimethyl-5-aminovalerate was associated with an increased risk of DR, and myristoleate (14:1n5), palmitoleate (16:1n7), and 5-dodecenoate (12:1n7) with a decreased risk of DR. Sphingomyelin (d18:2/24:2), a sphingolipid, was significantly associated with a decreased risk of DR. Carboxylic acid maleate and organic compounds 3-hydroxypyridine sulfate, 4-vinylphenol sulfate, 4-ethylcatechol sulfate, and dimethyl sulfone were significantly associated with an increased risk of DR. CONCLUSION: Our study is the first large population-based longitudinal study to identify metabolites for DR. We found multiple metabolites associated with an increased and decreased risk for DR from several different metabolic pathways.

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits.

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Metabolite Signature of Simvastatin Treatment Involves Multiple Metabolic Pathways.

Statins inhibit the 3-hydroxy-3-methylglutaryl-CoA reductase enzyme and are the most widely used medication for hypercholesterolemia. Previous studies on the metabolite signature of simvastatin treatment have included only a small number of metabolites. We performed a high-throughput liquid chromatography-tandem mass spectroscopy profiling on the effects of simvastatin treatment on 1098 metabolite concentrations in the participants of the METSIM (Metabolic Syndrome In Men) study including 1332 participants with simvastatin treatment and 6200 participants without statin treatment. We found that simvastatin exerts profound pleiotropic effects on different metabolite pathways, affecting not only lipids, but also amino acids, peptides, nucleotides, carbohydrates, co-factors, vitamins, and xenobiotics. We identified 321 metabolites significantly associated with simvastatin treatment, and 313 of these metabolites were novel. Our study is the first comprehensive evaluation of the metabolic signature of simvastatin treatment in a large population-based study.

Effects of SLCO1B1 Genetic Variant on Metabolite Profile in Participants on Simvastatin Treatment.

Organic-anion-transporting polypeptide 1B1 (OATP1B1), encoded by the solute carrier organic anion transporter family member 1B1 gene (SLCO1B1), is highly expressed in the liver and transports several endogenous metabolites into the liver, including statins. Previous studies have not investigated the association of SLCO1B1 rs4149056 variant with the risk of type 2 diabetes (T2D) or determined the metabolite signature of the C allele of SLCO1B1 rs4149056 (SLCO1B1 rs4149056-C allele) in a large randomly selected population. SLCO1B1 rs4149056-C inhibits OATP1B1 transporter and is associated with increased levels of blood simvastatin concentrations. Our study is to first to show that SLCO1B1 rs4149056 variant is not significantly associated with the risk of T2D, suggesting that simvastatin has a direct effect on the risk of T2D. Additionally, we investigated the effects of SLCO1B1 rs4149056-C on plasma metabolite concentrations in 1373 participants on simvastatin treatment and in 1368 age- and body-mass index (BMI)-matched participants without any statin treatment. We found 31 novel metabolites significantly associated with SLCO1B1 rs4149056-C in the participants on simvastatin treatment and in the participants without statin treatment. Simvastatin decreased concentrations of dicarboxylic acids, such as docosadioate and dodecanedioate, that may increase beta- and peroxisomal oxidation and increased the turnover of cholesterol into bile acids, resulting in a decrease in steroidogenesis due to limited availability of cholesterol for steroid synthesis. Our findings suggest that simvastatin exerts its effects on the lowering of low-density lipoprotein (LDL) cholesterol concentrations through several distinct pathways in the carriers of SLCO1B1 rs4149056-C, including dicarboxylic acids, bile acids, steroids, and glycerophospholipids.

Metabolite Signature in the Carriers of Pathogenic Genetic Variants for Cardiomyopathy: A Population-Based METSIM Study.

Hypertrophic (HCM) and dilated (DCM) cardiomyopathies are among the leading causes of sudden cardiac death. We identified 38 pathogenic or likely pathogenic variant carriers for HCM in three sarcomere genes (MYH7, MYBPC3, TPMI) among 9.928 participants of the METSIM Study having whole exome sequencing data available. Eight of them had a clinical diagnosis of HCM. We also identified 20 pathogenic or likely pathogenic variant carriers for DCM in the TTN gene, and six of them had a clinical diagnosis of DCM. The aim of our study was to investigate the metabolite signature in the carriers of the pathogenic or likely pathogenic genetic variants for HCM and DCM, compared to age- and body-mass-index-matched controls. Our novel findings were that the carriers of pathogenic or likely pathogenic variants for HCM had significantly increased concentrations of bradykinin (des-arg 9), vanillactate, and dimethylglycine and decreased concentrations of polysaturated fatty acids (PUFAs) and lysophosphatidylcholines compared with the controls without HCM. Additionally, our novel findings were that the carriers of pathogenic or likely pathogenic variants for DCM had significantly decreased concentrations of 1,5-anhydrogluticol, histidine betaine, N-acetyltryptophan, and methylsuccinate and increased concentrations of trans-4-hydroxyproline compared to the controls without DCM. Our population-based study shows that the metabolite signature of the genetic variants for HCM and DCM includes several novel metabolic pathways not previously described.

The "Common Soil Hypothesis" Revisited-Risk Factors for Type 2 Diabetes and Cardiovascular Disease.

The prevalence and the incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance, as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic variants in interaction with lifestyle and environmental factors. Cardiovascular disease (CVD) is the major cause of morbidity and mortality. Detailed understanding of molecular mechanisms underlying in CVD events is still largely missing. Several risk factors are shared between T2D and CVD, including obesity, insulin resistance, dyslipidemia, and hyperglycemia. CVD can precede the development of T2D, and T2D is a major risk factor for CVD, suggesting that both conditions have common genetic and environmental antecedents and that they share "common soil". We analyzed the relationship between the risk factors for T2D and CVD based on genetics and population-based studies with emphasis on Mendelian randomization studies.

Metabolite Signature of Albuminuria Involves Amino Acid Pathways in 8661 Finnish Men Without Diabetes.

OBJECTIVE: To investigate the metabolite signature of albuminuria in individuals without diabetes or chronic kidney disease to identify possible mechanisms that result in increased albuminuria and elevated risk of type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: The study cohort was a population-based Metabolic Syndrome In Men (METSIM) study including 8861 middle-aged and elderly Finnish men without diabetes or chronic kidney disease at baseline. A total of 5504 men participated in a 7.5-year follow-up study, and 5181 of them had metabolomics data measured by Metabolon's ultrahigh performance liquid chromatography-tandem mass spectroscopy. RESULTS: We found 32 metabolites significantly (P < 5.8 × 10-5) and positively associated with the urinary albumin excretion (UAE) rate. These metabolites were especially downstream metabolites in the amino acid metabolism pathways (threonine, phenylalanine, leucine, arginine). In our 7.5-year follow-up study, UAE was significantly associated with a 19% increase (hazard ratio 1.19; 95% confidence interval, 1.13-1.25) in the risk of T2D after the adjustment for confounding factors. Conversion to diabetes was more strongly associated with a decrease in insulin secretion than a decrease in insulin sensitivity. CONCLUSIONS: Metabolic signature of UAE included multiple metabolites, especially from the amino acid metabolism pathways known to be associated with low-grade inflammation, and accumulation of reactive oxygen species that play an important role in the pathogenesis of UAE. These metabolites were primarily associated with an increase in UAE and were secondarily associated with a decrease in insulin secretion and insulin sensitivity, resulting in an increased risk of incident T2D.

Novel biomarkers associated with incident heart failure in 10 106 Finnish men.

AIMS: There are only a few studies on novel biomarkers for incident heart failure (HF). We investigated the association of multiple circulating biomarkers with incident HF in a large prospective population-based study. METHODS AND RESULTS: Conventional risk factors and inflammatory biomarkers were measured, and systemic metabolic measures determined by a high-throughput serum nuclear magnetic resonance platform in a population-based Metabolic Syndrome in Men study including 10 106 Finnish men without HF at baseline. During an 8.8 year follow-up, 172 (1.7%) participants developed HF. Adiponectin, high-sensitivity C-reactive protein (hs-CRP), glycoprotein acetyls, alanine, phenylalanine, glycerol, and pyruvate were associated with incident HF in unadjusted Cox regression analyses, in addition to age, systolic blood pressure, body mass index (BMI), waist circumference, fasting plasma glucose and insulin, haemoglobin A1c (HbA1c), and urinary albumin excretion rate (UAER). After adjustment for age, BMI, diabetes, and statin medication, only adiponectin [hazard ratio (HR) 1.18 (1.10-1.26, P = 4.1E-08)], pyruvate [HR 1.38 (1.28-1.50, P = 8.2E-05)], and UAER [HR 1.15 (1.11-1.18, P = 7.8E-06)] remained statistically significant. In principal component analysis of biomarkers associated with HF in univariate Cox regression analysis, we identified six components, explaining 61.7% of total variance. Four principal components, one with significant loadings on waist, BMI, fasting plasma insulin, interleukin 1 receptor antagonist, and hs-CRP; another on pyruvate, glycoprotein acetyls, alanine, glycerol and HbA1c; third on age and glomerular filtration rate; and fourth on systolic blood pressure, UAER, and adiponectin, significantly associated with incident HF. CONCLUSIONS: Several novel metabolic and inflammatory biomarkers were associated with incident HF, suggesting early activation of respective pathways in the pathogenesis of HF.

The Association of 9 Amino Acids With Cardiovascular Events in Finnish Men in a 12-Year Follow-up Study.

BACKGROUND AND AIMS: To investigate the significance of 9 amino acids as risk factors for incident cardiovascular disease events in 9584 Finnish men. MATERIALS AND METHODS: A total of 9584 men (age 57.4 ± 7.0 years, body mass index 27.2 ± 4.2 kg/m2) from the Metabolic Syndrome in Men study without cardiovascular disease and type 1 diabetes at baseline were included in this study. A total of 662 coronary artery disease (CAD) events, 394 ischemic stroke events, and 966 cardiovascular disease (CVD; CAD and stroke combined) events were recorded in a 12.3-year follow-up. Amino acids were measured using nuclear magnetic resonance platform. RESULTS: In Cox regression analysis, phenylalanine and tyrosine were significantly associated with increased risk of CAD and CVD events, and phenylalanine with increased risk of ischemic stroke after the adjustment for confounding factors. Glutamine was significantly associated with decreased risk of stroke and CVD events and nominally with CAD events. Alanine was nominally associated with CAD events. CONCLUSION: We identified alanine as a new amino acid associated with increased risk of CAD and glutamine as a new amino acid associated with decreased risk of ischemic stroke. We also confirmed that phenylalanine and tyrosine were associated with CAD, ischemic stroke, and CVD events.

Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study.

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). ß-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.

Microbiota-Related Metabolites and the Risk of Type 2 Diabetes.

OBJECTIVE: Recent studies have highlighted the significance of the microbiome in human health and disease. Changes in the metabolites produced by microbiota have been implicated in several diseases. Our objective was to identify microbiome metabolites that are associated with type 2 diabetes. RESEARCH DESIGN AND METHODS: Our study included 5,181 participants from the cross-sectional Metabolic Syndrome in Men (METSIM) study that included Finnish men (age 57 ± 7 years, BMI 26.5 ± 3.5 kg/m2) having metabolomics data available. Metabolomics analysis was performed based on fasting plasma samples. On the basis of an oral glucose tolerance test, Matsuda ISI and disposition index values were calculated as markers of insulin sensitivity and insulin secretion. A total of 4,851 participants had a 7.4-year follow-up visit, and 522 participants developed type 2 diabetes. RESULTS: Creatine, 1-palmitoleoylglycerol (16:1), urate, 2-hydroxybutyrate/2-hydroxyisobutyrate, xanthine, xanthurenate, kynurenate, 3-(4-hydroxyphenyl)lactate, 1-oleoylglycerol (18:1), 1-myristoylglycerol (14:0), dimethylglycine, and 2-hydroxyhippurate (salicylurate) were significantly associated with an increased risk of type 2 diabetes. These metabolites were associated with decreased insulin secretion or insulin sensitivity or both. Among the metabolites that were associated with a decreased risk of type 2 diabetes, 1-linoleoylglycerophosphocholine (18:2) significantly reduced the risk of type 2 diabetes. CONCLUSIONS: Several novel and previously reported microbial metabolites related to the gut microbiota were associated with an increased risk of incident type 2 diabetes, and they were also associated with decreased insulin secretion and insulin sensitivity. Microbial metabolites are important biomarkers for the risk of type 2 diabetes.

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

BACKGROUND: The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. METHODS: Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. RESULTS: We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. CONCLUSIONS: Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

An intronic variant in the GCKR gene is associated with multiple lipids.

Previous studies have shown that an intronic variant rs780094 of the GCKR gene (glucokinase regulatory protein) is significantly associated with several metabolites, but the associations of this genetic variant with different lipids is largely unknown. Therefore, we applied metabolomics approach to measure metabolites in a large Finnish population sample (METSIM study) to investigate their associations with rs780094 of GCKR. We measured metabolites by mass spectrometry from 5,181 participants. P < 5.8 × 10-5 was considered as statistically significant given 857 metabolites included in statistical analyses. We found novel negative associations of the T allele of GCKR rs780094 with serine and threonine, and positive associations with two metabolites of tryptophan, indolelactate and N-acetyltryptophan. Additionally, we found novel significant positive associations of this genetic variant with 12 glycerolipids and 19 glycerophospholipids. Significant negative associations were found for three glycerophospholipids (all plasmalogen-cholines), and two sphingolipids. Significant novel associations were also found with gamma-glutamylthreonine, taurocholenate sulfate, and retinol. Our study adds new information about the pleiotropy of the GCKR gene, and shows the associations of the T allele of GCKR rs780094 with lipids.

Nine Amino Acids Are Associated With Decreased Insulin Secretion and Elevated Glucose Levels in a 7.4-Year Follow-up Study of 5,181 Finnish Men.

Several amino acids (AAs) have been shown to be associated with insulin resistance and increased risk of type 2 diabetes, but no previous studies have investigated the association of AAs with insulin secretion in a longitudinal setting. Our study included 5,181 participants of the cross-sectional METabolic Syndrome In Men (METSIM) study having metabolomics data on 20 AAs. A total of 4,851 had a 7.4-year follow-up visit. Nine AAs (phenylalanine, tryptophan, tyrosine, alanine, isoleucine, leucine, valine, aspartate, and glutamate) were significantly (P < 5.8 × 10-5) associated with decreases in insulin secretion (disposition index) and the elevation of fasting or 2-h glucose levels. Five of these AAs (tyrosine, alanine, isoleucine, aspartate, and glutamate) were also found to be significantly associated with an increased risk of incident type 2 diabetes after adjustment for confounding factors. Our study is the first population-based large cohort to report that AAs are associated not only with insulin resistance but also with decreased insulin secretion.