Imperial College London

DrIbrahimKaraman

Faculty of MedicineSchool of Public Health

Honorary Research Associate
 
 
 
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Contact

 

+44 (0)20 7594 3281i.karaman Website

 
 
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Location

 

155Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
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24 results found

Pan X-F, Yang JJ, Shu X-O, Moore SC, Palmer ND, Guasch-Ferré M, Herrington DM, Harada S, Eliassen H, Wang TJ, Gerszten RE, Albanes D, Tzoulaki I, Karaman I, Elliott P, Zhu H, Wagenknecht LE, Zheng W, Cai H, Cai Q, Matthews CE, Menni C, Meyer KA, Lipworth LP, Ose J, Fornage M, Ulrich CM, Yu Det al., 2021, Associations of circulating choline and its related metabolites with cardiometabolic biomarkers: an international pooled analysis, American Journal of Clinical Nutrition, Vol: 114, Pages: 893-906, ISSN: 0002-9165

BACKGROUND: Choline is an essential nutrient; however, the associations of choline and its related metabolites with cardiometabolic risk remain unclear. OBJECTIVE: We examined the associations of circulating choline, betaine, carnitine, and dimethylglycine (DMG) with cardiometabolic biomarkers and their potential dietary and nondietary determinants. METHODS: The cross-sectional analyses included 32,853 participants from 17 studies, who were free of cancer, cardiovascular diseases, chronic kidney diseases, and inflammatory bowel disease. In each study, metabolites and biomarkers were log-transformed and standardized by means and SDs, and linear regression coefficients (β) and 95% CIs were estimated with adjustments for potential confounders. Study-specific results were combined by random-effects meta-analyses. A false discovery rate <0.05 was considered significant. RESULTS: We observed moderate positive associations of circulating choline, carnitine, and DMG with creatinine [β (95% CI): 0.136 (0.084, 0.188), 0.106 (0.045, 0.168), and 0.128 (0.087, 0.169), respectively, for each SD increase in biomarkers on the log scale], carnitine with triglycerides (β = 0.076; 95% CI: 0.042, 0.109), homocysteine (β = 0.064; 95% CI: 0.033, 0.095), and LDL cholesterol (β = 0.055; 95% CI: 0.013, 0.096), DMG with homocysteine (β = 0.068; 95% CI: 0.023, 0.114), insulin (β = 0.068; 95% CI: 0.043, 0.093), and IL-6 (β = 0.060; 95% CI: 0.027, 0.094), but moderate inverse associations of betaine with triglycerides (β = -0.146; 95% CI: -0.188, -0.104), insulin (β = -0.106; 95% CI: -0.130, -0.082), homocysteine (β = -0.097; 95% CI: -0.149, -0.045), and total cholesterol (β = -0.074; 95% CI: -0.102, -0.047). In the whole pooled population, no dietary factor was associated with circulating choline; red meat intake was associated with circulating carnitine [β = 0.092 (0.042, 0.142) for a 1 serving/d increase], whereas plant prot

Journal article

Yang JJ, Tzoulaki I, Karaman I, Elliott P, Yu Det al., 2021, Circulating trimethylamine N-oxide (TMAO) in association with diet and cardiometabolic biomarkers: an international pooled analysis, American Journal of Clinical Nutrition, Vol: 113, Pages: 1145-1156, ISSN: 0002-9165

Background: Trimethylamine N-oxide (TMAO), a diet-derived, gut microbial-host co-metabolite, has been linked to cardiometabolic diseases. However, the relationships remain unclear between diet, TMAO, and cardiometabolic health in general populations from different regions and ethnicities. Objective: To examine associations of circulating TMAO with dietary and cardiometabolic factors in a pooled analysis of 16 population-based studies from the US, Europe, and Asia.Design: Included were 32,166 adults (16,269 White, 13,293 Asian, 1,247 Hispanic/Latino, and 1,236 Black) without cardiovascular disease, cancer, chronic kidney disease, or inflammatory bowel disease. Linear regression coefficients (β) were computed for standardized TMAO with harmonized variables. Study-specific results were combined by random-effects meta-analysis. False discovery rate<0.10 was considered significant. Results: After adjustment for potential confounders, circulating TMAO was associated with intakes of animal protein and saturated fat (β=0.124 and 0.058, respectively, for 5%-energy increase) and with shellfish, total fish, eggs, and red meat (β=0.370, 0.151, 0.081, and 0.056, respectively, for 1-serving/day increase). Plant protein and nuts showed inverse associations (β=-0.126 for 5%-energy increase from plant protein and -0.123 for 1-serving/day of nuts). Although the animal protein-TMAO association was consistent across populations, fish and shellfish associations were stronger among Asians (β=0.285 and 0.578), and egg and red meat associations were more prominent among Americans (β=0.153 and 0.093). Besides, circulating TMAO was positively associated with creatinine (β=0.131 per standard deviation increase in log-TMAO), homocysteine (β=0.065), insulin (β=0.048), HbA1c (β=0.048), and glucose (β=0.023), while inversely associated with HDL-cholesterol (β=-0.047) and blood pressure (β=-0.030). Each TMAO-biomarker association

Journal article

Wu C-T, Wang Y, Wang Y, Ebbels T, Karaman I, Graca G, Pinto R, Herrington DM, Wang Y, Yu Get al., 2020, Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection, BIOINFORMATICS, Vol: 36, Pages: 2862-2871, ISSN: 1367-4803

Journal article

Tzoulaki I, Castagné R, Boulangé CL, Karaman I, Chekmeneva E, Evangelou E, Ebbels TMD, Kaluarachchi MR, Chadeau-Hyam M, Mosen D, Dehghan A, Moayyeri A, Ferreira DLS, Guo X, Rotter JI, Taylor KD, Kavousi M, De Vries PS, Lehne B, Loh M, Hofman A, Nicholson JK, Chambers J, Gieger C, Holmes E, Tracy R, Kooner J, Greenland P, Franco OH, Herrington D, Lindon JC, Elliott Pet al., 2019, Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease, European Heart Journal, Vol: 40, Pages: 2883-2896, ISSN: 1522-9645

Aims: To characterise serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and Results: We used untargeted one-dimensional (1D) serum metabolic profiling by proton (1H) nuclear magnetic resonance (NMR) spectroscopy among 3,867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3,569 participants from the Rotterdam and LOLIPOP Studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 NMR measured metabolites were associated with CAC and/or IMT, P =1.3x10-14 to 6.5x10-6 (discovery), P =4.2x10-14 to 4.4x10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched-chain and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide and lactate as well as apolipoprotein B (P <0.05). Conclusion: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclero

Journal article

Evangelou E, Gao H, Blakeley P, Pazoki R, Suzuki H, Elliott J, Karaman I, Jarvelin MR, Tzoulaki I, Bell JD, Matthews PM, Elliott Pet al., 2019, New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders, Nature Human Behaviour, Vol: 3, Pages: 950-961, ISSN: 2397-3374

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. Here we conducted a meta-analysis of genome-wide association studies of alcohol consumption (g d−1) from the UK Biobank, the Alcohol Genome-Wide Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Plus consortia, collecting data from 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 new common loci and investigated their potential functional importance using magnetic resonance imaging data and gene expression studies. We identify genetic pathways associated with alcohol consumption and suggest genetic mechanisms that are shared with neuropsychiatric disorders such as schizophrenia.

Journal article

Pazoki R, Evangelou E, Mosen-Ansorena D, Pinto R, Karaman I, Blakeley P, Gill D, Zuber V, Elliott P, Tzoulaki I, Dehghan Aet al., 2019, GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits, Nature Communications, Vol: 10, ISSN: 2041-1723

Urinary sodium and potassium excretion are associated with blood pressure (BP) and cardiovascular disease (CVD). The exact biological link between these traits is yet to be elucidated. Here, we identify 51 loci for sodium and 13 for potassium excretion in a large-scale genome-wide association study (GWAS) on urinary sodium and potassium excretion using data from 446,237 individuals of European descent from the UK Biobank study. We extensively interrogate the results using multiple analyses such as Mendelian randomization, functional assessment, co localization, genetic risk score, and pathway analyses. We identify a shared genetic component between urinary sodium and potassium expression and cardiovascular traits. Ingenuity pathway analysis shows that urinary sodium and potassium excretion loci are over represented in behavioural response to stimuli. Our study highlights pathways that are shared between urinary sodium and potassium excretion and cardiovascular traits.

Journal article

Neeland IJ, Boone SC, Mook-Kanamori DO, Ayers C, Smit RAJ, Tzoulaki I, Karaman I, Boulange C, Vaidya D, Punjabi N, Allison M, Herrington DM, Jukema JW, Rosendaal FR, Lamb HJ, van Dijk KW, Greenland P, de Mutsert Ret al., 2019, Metabolomics profiling of visceral adipose tissue: Results From MESA and the NEO study, Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease, Vol: 8, ISSN: 2047-9980

Background Identifying associations between serum metabolites and visceral adipose tissue ( VAT ) could provide novel biomarkers of VAT and insights into the pathogenesis of obesity-related diseases. We aimed to discover and replicate metabolites reflecting pathways related to VAT . Methods and Results Associations between fasting serum metabolites and VAT area (by computed tomography or magnetic resonance imaging) were assessed with cross-sectional linear regression of individual-level data from participants in MESA (Multi-Ethnic Study of Atherosclerosis; discovery, N=1103) and the NEO (Netherlands Epidemiology of Obesity) study (replication, N=2537). Untargeted 1H nuclear magnetic resonance metabolomics profiling of serum was performed in MESA, and metabolites were replicated in the NEO study using targeted 1H nuclear magnetic resonance spectroscopy. A total of 30 590 metabolomic spectral variables were evaluated. After adjustment for age, sex, race/ethnicity, socioeconomic status, smoking, physical activity, glucose/lipid-lowering medication, and body mass index, 2104 variables representing 24 nonlipid and 49 lipid/lipoprotein subclass metabolites remained significantly associated with VAT ( P=4.88×10-20-1.16×10-3). These included conventional metabolites, amino acids, acetylglycoproteins, intermediates of glucose and hepatic metabolism, organic acids, and subclasses of apolipoproteins, cholesterol, phospholipids, and triglycerides. Metabolites mapped to 31 biochemical pathways, including amino acid substrate use/metabolism and glycolysis/gluconeogenesis. In the replication cohort, acetylglycoproteins, branched-chain amino acids, lactate, glutamine (inversely), and atherogenic lipids remained associated with VAT ( P=1.90×10-35-8.46×10-7), with most associations remaining after additional adjustment for surrogates of VAT (glucose level, waist circumference, and serum triglycerides), reflecting novel independent associations. Conclusion

Journal article

Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Khonsari PE, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone S-A, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck Cet al., 2019, PhenoMeNal: Processing and analysis of metabolomics data in the cloud, GigaScience, Vol: 8, ISSN: 2047-217X

Background: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent-and sometimes incompatible-analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm. Conclusions: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible and shareable metabolomics data analysis platforms which are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adap

Journal article

Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Fu LN, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Edwards DRV, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins BP, Qian Y, Sargurupremraj M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kahonen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu Y, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx BWJH, Perolet al., 2018, Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018), NATURE GENETICS, Vol: 50, Pages: 1755-1755, ISSN: 1061-4036

Journal article

Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Fu LN, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Edwards DRV, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins BP, Qian Y, Sargurupremraj M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kahonen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu Y, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx BWJH, Perolet al., 2018, Publisher correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits, Nature Genetics, Vol: 50, Pages: 1755-1755, ISSN: 1061-4036

Correction to: Nature Genetics https://doi.org/10.1038/s41588-018-0205-x, published online 17 September 2018.

Journal article

Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Fu LN, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Edwards DRV, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins BP, Qian Y, Sargurupremraj M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kahonen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu Y, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx BWJH, Perolet al., 2018, Genetic analysis of over one million people identifies 535 new loci associated with blood pressure traits, Nature Genetics, Vol: 50, Pages: 1412-1425, ISSN: 1061-4036

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

Journal article

Karaman I, Climaco Pinto R, Graça G, 2018, Metabolomics Data Preprocessing: From Raw Data to Features for Statistical Analysis, Comprehensive Analytical Chemistry, Vol: 82, Pages: 197-225, ISSN: 0166-526X

© 2018 Elsevier B.V. Data preprocessing constitutes one of the most important stages in an untargeted metabolomics study. Every step of the workflow of data preparation from its ‘raw’ format to a table of metabolic features must be performed using clear and reproducible procedures to ensure that good datasets are produced. In this chapter, we will describe the general steps necessary to preprocess the complex datasets obtained in untargeted nuclear magnetic resonance spectroscopy and mass spectrometry-based metabolomics studies, from raw data to tables of features that can be used for statistical analysis.

Journal article

Kaluarachchi M, Boulangé C, Karaman I, Lindon JC, Ebbels T, Elliott P, Tracy R, Olson NCet al., 2018, A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS, Metabolomics, Vol: 14, ISSN: 1573-3882

Introduction:Differences in the metabolite profiles between serum and plasma are incompletely understood.Objectives:To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes.Methods:We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis.ResultsExplorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples.Conclusions:Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

Journal article

Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I, Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, Karaman I, Lepe MPS, O'Reilly PF, Knight J, Snieder H, Kato N, He J, Tai ES, Said MA, Porteous D, Alver M, Poulter N, Farrall M, Gansevoort RT, Padmanabhan S, Magi R, Stanton A, Connell J, Bakker SJL, Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A, Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M, Samani NJ, Howson JMM, Tobin MD, Munroe PB, Ehret GB, Wain LV, Barnes MR, Tzoulaki I, Caulfield MJ, Elliott Pet al., 2017, Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk (vol 49, pg 403, 2017), NATURE GENETICS, Vol: 49, Pages: 1558-1558, ISSN: 1061-4036

Journal article

castagne R, Boulange CL, Karaman I, campanella, Santos Ferreira DL, Kaluarachchi MR, lehne, Moayyeri A, Lewis MR, Spagou K, DOna AC, Evangelos V, Tracy R, Greenland P, Lindon JC, ebbels TMD, elliott, tzoulaki, Chadeau Met al., 2017, Improving visualisation and interpretation of metabolome-wide association studies (MWAS): an application in a population-based cohort using untargeted 1H NMR metabolic profiling., Journal of Proteome Research, Vol: 16, Pages: 3623-3633, ISSN: 1535-3893

1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Journal article

Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I, Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, Karaman I, Lepe MP, O'Reilly PF, Knight J, Snieder H, Kato N, He J, Tai ES, Said MA, Porteous D, Alver M, Poulter N, Farrall M, Gansevoort RT, Padmanabhan S, Mägi R, Stanton A, Connell J, Bakker SJ, Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A, Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M, Samani NJ, Howson JM, Tobin MD, Munroe PB, Ehret GB, Wain LV, International Consortium of Blood Pressure ICBP 1000G Analyses, BIOS Consortium, Lifelines Cohort Study, Understanding Society Scientific group, CHD Exome Consortium, ExomeBP Consortium, T2D-GENES Consortium, GoT2DGenes Consortium, Cohorts for Heart and Ageing Research in Genome Epidemiology CHARGE BP Exome Consortium, International Genomics of Blood Pressure iGEN-BP Consortium, UK Biobank CardioMetabolic Consortium BP working groupet al., 2017, Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk., Nature Genetics, Vol: 49, Pages: 403-415, ISSN: 1546-1718

Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide. We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK Biobank participants of European ancestry with independent replication in other cohorts, and robust validation of 107 independent loci. We also identify new independent variants at 11 previously reported blood pressure loci. In combination with results from a range of in silico functional analyses and wet bench experiments, our findings highlight new biological pathways for blood pressure regulation enriched for genes expressed in vascular tissues and identify potential therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure-raising genetic variants on future cardiovascular disease risk.

Journal article

Karaman I, 2017, Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis, Metabolomics: From Fundamentals to Clinical Applications, Editors: Sussulini, Publisher: Springer, ISBN: 9783319476568

From data acquisition to statistical analysis, metabolomics data need to undergo several processing steps, which are crucial for the data quality and interpretation of the results. In this chapter, methods for preprocessing, normalization, and pretreatment of metabolomics data generated from nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are presented and discussed. Preprocessing is reported for both NMR and MS analysis. The challenges in preprocessing such complex data are highlighted. Subsequently, normalization methods such as total area normalization, probabilistic quotient normalization, and quantile normalization are explained. Finally, several scaling and data transformation methods are discussed for metabolomics data pretreatment, which is an important step prior to statistical analysis.

Book chapter

Karaman I, Ferreira DL, Boulange CL, Kaluarachchi MR, Herrington D, Dona AC, Castagné R, Moayyeri A, Lehne B, Loh M, de Vries PS, Dehghan A, Franco O, Hofman A, Evangelou E, Tzoulaki I, Elliott P, Lindon JC, Ebbels TMet al., 2016, A workflow for integrated processing of multi-cohort untargeted 1H NMR metabolomics data in large scale metabolic epidemiology, Journal of Proteome Research, Vol: 15, Pages: 4188-4194, ISSN: 1535-3907

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines which can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from approximately 8000 individuals, in 3 cohorts, profiled by 6 assays in 2 phases using both 1H-NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling’s T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR datasets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling’s T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636 indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multi-faceted study.

Journal article

Ingerslev AK, Karaman I, Bagcioglu M, Kohler A, Thei PK, Knudsen KEB, Hedemann MSet al., 2015, Whole Grain Consumption Increases Gastrointestinal Content of Sulfate-Conjugated Oxylipins in Pigs - A Multicompartmental Metabolomics Study, JOURNAL OF PROTEOME RESEARCH, Vol: 14, Pages: 3095-3110, ISSN: 1535-3893

Journal article

Karaman I, Norskov NP, Yde CC, Hedemann MS, Knudsen KEB, Kohler Aet al., 2015, Sparse multi-block PLSR for biomarker discovery when integrating data from LC-MS and NMR metabolomics, METABOLOMICS, Vol: 11, Pages: 367-379, ISSN: 1573-3882

Journal article

Karaman I, Qannari EM, Martens H, Hedemann MS, Knudsen KEB, Kohler Aet al., 2013, Comparison of Sparse and Jack-knife partial least squares regression methods for variable selection, CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, Vol: 122, Pages: 65-77, ISSN: 0169-7439

Journal article

Brokner C, Knudsen KEB, Karaman I, Eybye KL, Tauson AHet al., 2012, Chemical and physicochemical characterisation of various horse feed ingredients, ANIMAL FEED SCIENCE AND TECHNOLOGY, Vol: 177, Pages: 86-97, ISSN: 0377-8401

Journal article

Uner B, Karaman I, Tanriverdi H, Ozdemir Det al., 2011, Determination of lignin and extractive content of Turkish Pine (Pinus brutia Ten.) trees using near infrared spectroscopy and multivariate calibration, WOOD SCIENCE AND TECHNOLOGY, Vol: 45, Pages: 121-134, ISSN: 0043-7719

Journal article

Uner B, Karaman I, Tanriverdi H, Oezdemir Det al., 2009, Prediction of Lignin and Extractive Content of Pinus nigra Arnold. var. Pallasiana Tree Using Near Infrared Spectroscopy and Multivariate Calibration, JOURNAL OF WOOD CHEMISTRY AND TECHNOLOGY, Vol: 29, Pages: 24-42, ISSN: 0277-3813

Journal article

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