Publications by authors named "Ayse Demirkan"

75 Publications

The trans-ancestral genomic architecture of glycemic traits.

Nat Genet 2021 06 31;53(6):840-860. Epub 2021 May 31.

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

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 < 5 × 10), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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http://dx.doi.org/10.1038/s41588-021-00852-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610958PMC
June 2021

Large-scale association analyses identify host factors influencing human gut microbiome composition.

Nat Genet 2021 02 18;53(2):156-165. Epub 2021 Jan 18.

Department of Twin Research & Genetic Epidemiology, King's College London, London, UK.

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 × 10) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10 < P < 5 × 10) 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.
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http://dx.doi.org/10.1038/s41588-020-00763-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515199PMC
February 2021

Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity.

Diabetes 2020 12 11;69(12):2806-2818. Epub 2020 Sep 11.

Department of Biostatistics, Boston University School of Public Health, Boston, MA.

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.
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http://dx.doi.org/10.2337/db20-0070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7679778PMC
December 2020

CDH6 and HAGH protein levels in plasma associate with Alzheimer's disease in APOE ε4 carriers.

Sci Rep 2020 05 19;10(1):8233. Epub 2020 May 19.

Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.

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 < 1 × 10). The HAGH protein was not detected in CSF. GWAS of plasma CDH6 protein levels showed significant association with a cis-regulatory locus (rs111283466, P = 1.92 × 10). CDH6 protein is implicated in cell adhesion and synaptogenesis while HAGH protein is related to the oxidative stress pathway. Our findings suggest that these pathways may be altered during presymptomatic AD and that CDH6 and HAGH may be new blood-based biomarkers.
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http://dx.doi.org/10.1038/s41598-020-65038-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237496PMC
May 2020

Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose.

PLoS One 2020 7;15(5):e0230815. Epub 2020 May 7.

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230815PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205201PMC
August 2020

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas.

Nat Med 2020 01 13;26(1):110-117. Epub 2020 Jan 13.

Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.

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/).
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http://dx.doi.org/10.1038/s41591-019-0722-xDOI Listing
January 2020

Lipidomic profiling identifies signatures of metabolic risk.

EBioMedicine 2020 Jan 24;51:102520. Epub 2019 Dec 24.

Framingham Heart Study, Framingham, MA, United States; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States. Electronic address:

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.
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http://dx.doi.org/10.1016/j.ebiom.2019.10.046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938899PMC
January 2020

Metabolomics Profile in Depression: A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls.

Biol Psychiatry 2020 03 29;87(5):409-418. Epub 2019 Aug 29.

Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.
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http://dx.doi.org/10.1016/j.biopsych.2019.08.016DOI Listing
March 2020

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.

Nat Genet 2019 10 2;51(10):1459-1474. Epub 2019 Oct 2.

Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden.

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.
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http://dx.doi.org/10.1038/s41588-019-0504-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6858555PMC
October 2019

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

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

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

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

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

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

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

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

Lipidomics, Atrial Conduction, and Body Mass Index.

Circ Genom Precis Med 2019 07 15;12(7):e002384. Epub 2019 Jul 15.

Institute for Biomedicine, Eurac Research, Affiliated to the University of Lübeck, Bolzano, Italy (G.P., L.F., R.M., V.V., A.A.H., P.P.P., A.R., C.P.).

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=1.1×10) 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=8.3×10) 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.
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http://dx.doi.org/10.1161/CIRCGEN.118.002384DOI Listing
July 2019

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis.

Nat Commun 2019 06 13;10(1):2581. Epub 2019 Jun 13.

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.

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.
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http://dx.doi.org/10.1038/s41467-019-10487-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565679PMC
June 2019

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology.

Am J Hum Genet 2019 07 6;105(1):15-28. Epub 2019 Jun 6.

The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA.

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.
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http://dx.doi.org/10.1016/j.ajhg.2019.05.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612516PMC
July 2019

A catalog of genetic loci associated with kidney function from analyses of a million individuals.

Nat Genet 2019 06 31;51(6):957-972. Epub 2019 May 31.

Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden.

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.
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http://dx.doi.org/10.1038/s41588-019-0407-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698888PMC
June 2019

Large-scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine.

Neurology 2019 04 3;92(16):e1899-e1911. Epub 2019 Apr 3.

From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands.

Objective: To identify a plasma metabolomic biomarker signature for migraine.

Methods: 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.

Results: 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.

Conclusions: Metabolic profiling of plasma yielded alterations in HDL metabolism in migraine patients and decreased omega-3 fatty acids only in male migraineurs.
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http://dx.doi.org/10.1212/WNL.0000000000007313DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550500PMC
April 2019

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Nat Commun 2019 01 22;10(1):376. Epub 2019 Jan 22.

Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, 01246903, SP, Brazil.

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.
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http://dx.doi.org/10.1038/s41467-018-08008-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342931PMC
January 2019

Increased High-Density Lipoprotein Levels Associated with Age-Related Macular Degeneration: Evidence from the EYE-RISK and European Eye Epidemiology Consortia.

Ophthalmology 2019 03 10;126(3):393-406. Epub 2018 Oct 10.

Section of Academic Ophthalmology, School of Life Course Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom; Department of Twin Research & Genetic Epidemiology, King's College London, St. Thomas' Hospital, London, United Kingdom.

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 × 10), but lipase C risk variants (rs2043085, rs2070895) were associated in an opposite way (P = 1.0 × 10 and P = 1.6 × 10).

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.
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http://dx.doi.org/10.1016/j.ophtha.2018.09.045DOI Listing
March 2019

Large-scale whole-exome sequencing association studies identify rare functional variants influencing serum urate levels.

Nat Commun 2018 10 12;9(1):4228. Epub 2018 Oct 12.

UTHealth School of Public Health, Houston, Texas, 77030, USA.

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) and SLC2A9 (p = 4.5 × 10). Gout risk in rare SLC22A12 variant carriers is halved (OR = 0.5, p = 4.9 × 10). 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.
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http://dx.doi.org/10.1038/s41467-018-06620-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6185909PMC
October 2018

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

Nat Genet 2018 04 9;50(4):559-571. Epub 2018 Apr 9.

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

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 < 2.2 × 10); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.
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http://dx.doi.org/10.1038/s41588-018-0084-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898373PMC
April 2018

Metabolic profiling of intra- and extracranial carotid artery atherosclerosis.

Atherosclerosis 2018 05 8;272:60-65. Epub 2018 Mar 8.

Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands. Electronic address:

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.

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.

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 × 10). 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, glycoprotein acetyls were associated with calcification in both studied vessel beds. These associations were most prominent in men.

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.
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http://dx.doi.org/10.1016/j.atherosclerosis.2018.03.015DOI Listing
May 2018

Association of branched-chain amino acids and other circulating metabolites with risk of incident dementia and Alzheimer's disease: A prospective study in eight cohorts.

Alzheimers Dement 2018 06 6;14(6):723-733. Epub 2018 Mar 6.

Department of Neurology, Boston University School of Medicine, Boston, MA, USA; The Framingham Heart Study, Framingham, MA, USA; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA. Electronic address:

Introduction: Metabolite, lipid, and lipoprotein lipid profiling can provide novel insights into mechanisms underlying incident dementia and Alzheimer's disease.

Methods: 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.

Results: 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.

Discussion: Further studies can clarify whether these molecules play a causal role in dementia pathogenesis or are merely markers of early pathology.
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http://dx.doi.org/10.1016/j.jalz.2018.01.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6082422PMC
June 2018

A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy.

BMC Med Genomics 2018 03 5;11(1):22. Epub 2018 Mar 5.

Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.

Background: Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them.

Methods: The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data.

Results: We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied.

Conclusions: We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.
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http://dx.doi.org/10.1186/s12920-018-0339-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838853PMC
March 2018

Circulating metabolites and general cognitive ability and dementia: Evidence from 11 cohort studies.

Alzheimers Dement 2018 06 6;14(6):707-722. Epub 2018 Jan 6.

Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.

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.
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http://dx.doi.org/10.1016/j.jalz.2017.11.012DOI Listing
June 2018

Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness.

Nat Commun 2017 07 12;8:16015. Epub 2017 Jul 12.

MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK.

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<5 × 10) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.
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http://dx.doi.org/10.1038/ncomms16015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510175PMC
July 2017

Blood-based metabolic signatures in Alzheimer's disease.

Alzheimers Dement (Amst) 2017 6;8:196-207. Epub 2017 Sep 6.

Neurochemistry Laboratory and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center Amsterdam, Amsterdam, The Netherlands.

Introduction: Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers.

Methods: 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).

Results: 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 () ε4 negative AD patients was less cohesive compared with the network for ε4 positive AD patients.

Discussion: Multiple signatures point to various promising peripheral markers for further validation. The network differences in AD patients according to genotype may reflect different pathways to AD.
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http://dx.doi.org/10.1016/j.dadm.2017.07.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607205PMC
September 2017

A Mendelian Randomization Study of Metabolite Profiles, Fasting Glucose, and Type 2 Diabetes.

Diabetes 2017 11 28;66(11):2915-2926. Epub 2017 Aug 28.

Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands

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 ( = 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.
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http://dx.doi.org/10.2337/db17-0199DOI Listing
November 2017

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study.

Metabolomics 2017 28;13(9):104. Epub 2017 Jul 28.

Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.

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 <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI < 25 kg/m) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC = 0.89).

Conclusions: Our novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.
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http://dx.doi.org/10.1007/s11306-017-1239-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533833PMC
July 2017
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