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1. Systematic evaluation of the association between hemoglobin levels and metabolic profile implicates beneficial effects of hypoxia

Author: Auvinen, J. (Juha), Tapio, J. (Joona), Karhunen, V. (Ville), Kettunen, J. (Johannes), Serpi, R. (Raisa), Dimova, E. Y. (Elitsa Y.), Gill, D. (Dipender), Soininen, P. (Pasi), Tammelin, T. (Tuija), Mykkänen, J. (Juha), Puukka, K. (Katri), Kähönen, M. (Mika), Raitoharju, E. (Emma), Lehtimäki, T. (Terho), Ala-Korpela, M. (Mika), Raitakari, O. T. (Olli T.), Keinänen-Kiukaanniemi, S. (Sirkka), Järvelin, M.-R. (Marjo-Riitta), Koivunen, P. (Peppi), Auvinen, J. (Juha), Tapio, J. (Joona), Karhunen, V. (Ville), Kettunen, J. (Johannes), Serpi, R. (Raisa), Dimova, E. Y. (Elitsa Y.), Gill, D. (Dipender), Soininen, P. (Pasi), Tammelin, T. (Tuija), Mykkänen, J. (Juha), Puukka, K. (Katri), Kähönen, M. (Mika), Raitoharju, E. (Emma), Lehtimäki, T. (Terho), Ala-Korpela, M. (Mika), Raitakari, O. T. (Olli T.), Keinänen-Kiukaanniemi, S. (Sirkka), Järvelin, M.-R. (Marjo-Riitta), and Koivunen, P. (Peppi)
Abstract: Activation of the hypoxia-inducible factor (HIF) pathway reprograms energy metabolism. Hemoglobin (Hb) is the main carrier of oxygen. Using its normal variation as a surrogate measure for hypoxia, we explored whether lower Hb levels could lead to healthier metabolic profiles in mice and humans (nn = 7175) and used Mendelian randomization (MR) to evaluate potential causality (n = 173,480). The results showed evidence for lower Hb levels being associated with lower body mass index, better glucose tolerance and other metabolic profiles, lower inflammatory load, and blood pressure. Expression of the key HIF target genes SLC2A4 and Slc2a1 in skeletal muscle and adipose tissue, respectively, associated with systolic blood pressure in MR analyses and body weight, liver weight, and adiposity in mice. Last, manipulation of murine Hb levels mediated changes to key metabolic parameters. In conclusion, low-end normal Hb levels may be favorable for metabolic health involving mild chronic activation of the HIF response.
Published: 2021

2. Metabolic profiles help discriminate mild cognitive impairment from dementia stage in Alzheimer’s disease

Author: Jääskeläinen, O. (Olli), Hall, A. (Anette), Tiainen, M. (Mika), van Gils, M. (Mark), Lötjönen, J. (Jyrki), Kangas, A. J. (Antti J.), Helisalmi, S. (Seppo), Pikkarainen, M. (Maria), Hallikainen, M. (Merja), Koivisto, A. (Anne), Hartikainen, P. (Päivi), Hiltunen, M. (Mikko), Ala-Korpela, M. (Mika), Soininen, P. (Pasi), Soininen, H. (Hilkka), Herukka, S.-K. (Sanna-Kaisa), Jääskeläinen, O. (Olli), Hall, A. (Anette), Tiainen, M. (Mika), van Gils, M. (Mark), Lötjönen, J. (Jyrki), Kangas, A. J. (Antti J.), Helisalmi, S. (Seppo), Pikkarainen, M. (Maria), Hallikainen, M. (Merja), Koivisto, A. (Anne), Hartikainen, P. (Päivi), Hiltunen, M. (Mikko), Ala-Korpela, M. (Mika), Soininen, P. (Pasi), Soininen, H. (Hilkka), and Herukka, S.-K. (Sanna-Kaisa)
Abstract: Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD). The metabolic profiles were quantified for 498 serum and CSF samples using proton nuclear magnetic resonance spectroscopy. The patient cohorts in this study were dementia (with a clinical AD diagnosis) (N = 359), mild cognitive impairment (MCI) (N = 96), and control patients with subjective memory complaints (N = 43). DSI classification was conducted for MCI (N = 51) and dementia (N = 214) patients with low CSF amyloid-β levels indicating AD pathology and controls without such amyloid pathology (N = 36). We saw that the conventional CSF markers of AD were better at classifying controls from both dementia and MCI patients. However, quantified metabolic subclasses were more effective in classifying MCI from dementia. Our results show the consistent effectiveness of traditional CSF biomarkers in AD diagnostics. However, these markers are relatively ineffective in differentiating between MCI and the dementia stage, where the quantified metabolomics data provided significant benefit.
Published: 2020

3. Total liver phosphatidylcholine content associates with non-alcoholic steatohepatitis and glycine N-methyltransferase expression

Author: Männistö, V. (Ville), Kaminska, D. (Dorota), Kärjä, V. (Vesa), Tiainen, M. (Mika), de Mello, V. D. (Vanessa D.), Hanhineva, K. (Kati), Soininen, P. (Pasi), Ala‐Korpela, M. (Mika), and Pihlajamäki, J. (Jussi)
Subjects: Proton magnetic resonance spectroscopy, Phosphatidylcholine, Phosphatidylethanolamine N-methyltransferase, Glycine N-methyltransferase, Nonalcoholic steatohepatitis, Non-alcoholic fatty liver disease
Abstract: Background & Aims: Alterations in liver phosphatidylcholine (PC) metabolism have been implicated in the pathogenesis of non‐alcoholic fatty liver disease (NAFLD). Although genetic variation in the phosphatidylethanolamine N‐methyltransferase (PEMT) enzyme synthesizing PC has been associated with disease, the functional mechanism linking PC metabolism to the pathogenesis of non‐alcoholic steatohepatitis (NASH) remains unclear. Methods: Serum PC levels and liver PC contents were measured using proton nuclear magnetic resonance (NMR) spectroscopy in 169 obese individuals [age 46.6 ± 10 (mean ± SD) years, BMI 43.3 ± 6 kg/m2, 53 men and 116 women] with histological assessment of NAFLD; 106 of these had a distinct liver phenotype. All subjects were genotyped for PEMT rs7946 and liver mRNA expression of PEMT and glycine N‐methyltransferase (GNMT) was analysed. Results: Liver PC content was lower in those with NASH (P = 1.8 x 10−6) while serum PC levels did not differ between individuals with NASH and normal liver (P = 0.591). Interestingly, serum and liver PC did not correlate (rs = −0.047, P = 0.557). Serum PC and serum cholesterol levels correlated strongly (rs = 0.866, P = 7.1 x 10−49), while liver PC content did not correlate with serum cholesterol (rs = 0.065, P = 0.413). Neither PEMT V175M genotype nor PEMT expression explained the association between liver PC content and NASH. Instead, liver GNMT mRNA expression was decreased in those with NASH (P = 3.8 x 10−4) and correlated with liver PC content (rs = 0.265, P = 0.001). Conclusions: Decreased liver PC content in individuals with the NASH is independent of PEMT V175M genotype and could be partly linked to decreased GNMT expression.
Published: 2019

4. Low serum high-density lipoprotein cholesterol levels associate with the C9orf72 repeat expansion in frontotemporal lobar degeneration patients

Author: Jääskeläinen, O. (Olli), Solje, E. (Eino), Hall, A. (Anette), Katisko, K. (Kasper), Korhonen, V. (Ville), Tiainen, M. (Mika), Kangas, A. J. (Antti J.), Helisalmi, S. (Seppo), Pikkarainen, M. (Maria), Koivisto, A. (Anne), Hartikainen, P. (Päivi), Hiltunen, M. (Mikko), Ala-Korpela, M. (Mika), Soininen, H. (Hilkka), Soininen, P. (Pasi), Haapasalo, A. (Annakaisa), Remes, A. M. (Anne M.), Herukka, S.-K. (Sanna-Kaisa), Jääskeläinen, O. (Olli), Solje, E. (Eino), Hall, A. (Anette), Katisko, K. (Kasper), Korhonen, V. (Ville), Tiainen, M. (Mika), Kangas, A. J. (Antti J.), Helisalmi, S. (Seppo), Pikkarainen, M. (Maria), Koivisto, A. (Anne), Hartikainen, P. (Päivi), Hiltunen, M. (Mikko), Ala-Korpela, M. (Mika), Soininen, H. (Hilkka), Soininen, P. (Pasi), Haapasalo, A. (Annakaisa), Remes, A. M. (Anne M.), and Herukka, S.-K. (Sanna-Kaisa)
Abstract: Decreased levels of serum high-density lipoprotein (HDL) cholesterol have previously been linked to systemic inflammation and neurodegenerative diseases, such as Alzheimer’s disease. Here, we aimed to analyze the lipoprotein profile and inflammatory indicators, the high-sensitivity C-reactive peptide (hs-CRP) and glycoprotein acetyls (GlycA), in sporadic and C9orf72 repeat expansion-associated frontotemporal lobar degeneration (FTLD) patients. The C9orf72 hexanucleotide repeat expansion is the most frequent genetic etiology underlying FTLD. The concentrations of different lipid measures in the sera of 67 FTLD patients (15 C9orf72 repeat expansion carriers), including GlycA, were analyzed by nuclear magnetic resonance spectroscopy. To verify the state of systemic inflammation, hs-CRP was also quantified from patient sera. We found that the total serum HDL concentration was decreased in C9orf72 repeat expansion carriers when compared to non-carriers. Moreover, decreased concentrations of HDL particles of different sizes and subclass were consistently observed. No differences were detected in the very low- and low-density lipoprotein subclasses between the C9orf72 repeat expansion carriers and non-carriers. Furthermore, hs-CRP and GlycA levels did not differ between the C9orf72 repeat expansion carriers and non-carriers. In conclusion, the HDL-related changes were linked with C9orf72 repeat expansion associated FTLD but were not seen to associate with systemic inflammation. The underlying reason for the HDL changes remains unclear.
Published: 2019

5. Nuclear magnetic resonance‐based metabolomics identifies phenylalanine as a novel predictor of incident heart failure hospitalisation:results from PROSPER and FINRISK 1997

Author: Delles, C. (Christian), Rankin, N. J. (Naomi J.), Boachie, C. (Charles), McConnachie, A. (Alex), Ford, I. (Ian), Kangas, A. (Antti), Soininen, P. (Pasi), Trompet, S. (Stella), Mooijaart, S. P. (Simon P.), Jukema, J. W. (J. Wouter), Zannad, F. (Faiez), Ala‐Korpela, M. (Mika), Salomaa, V. (Veikko), Havulinna, A. S. (Aki S.), Welsh, P. (Paul), Würtz, P. (Peter), and Sattar, N. (Naveed)
Subjects: Phenylalanine, FINRISK, Metabolomics, Heart failure, PROSPER, Advanced lipoprotein profiling
Abstract: Aims: We investigated the association between quantified metabolite, lipid and lipoprotein measures and incident heart failure hospitalisation (HFH) in the elderly, and examined whether circulating metabolic measures improve HFH prediction. Methods and results: Overall, 80 metabolic measures from the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial were measured by proton nuclear magnetic resonance spectroscopy (n = 5341; 182 HFH events during 2.7‐year follow‐up). We repeated the work in FINRISK 1997 (n = 7330; 133 HFH events during 5‐year follow‐up). In PROSPER, the circulating concentrations of 13 metabolic measures were found to be significantly different in those who were later hospitalised for heart failure after correction for multiple comparisons. These included creatinine, phenylalanine, glycoprotein acetyls, 3‐hydroxybutyrate, and various high‐density lipoprotein measures. In Cox models, two metabolites were associated with risk of HFH after adjustment for clinical risk factors and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP): phenylalanine [hazard ratio (HR) 1.29, 95% confidence interval (CI) 1.10–1.53; P = 0.002] and acetate (HR 0.81, 95% CI 0.68–0.98; P = 0.026). Both were retained in the final model after backward elimination. Compared to a model with established risk factors and NT‐proBNP, this model did not improve the C‐index but did improve the overall continuous net reclassification index (NRI 0.21; 95% CI 0.06–0.35; P = 0.007) due to improvement in classification of non‐cases (NRI 0.14; 95% CI 0.12–0.17; P
Published: 2018

6. Association of circulating metabolites with healthy diet and risk of cardiovascular disease:analysis of two cohort studies

Author: Akbaraly, T. (Tasnime), Würtz, P. (Peter), Singh-Manoux, A. (Archana), Shipley, M. J. (Martin J.), Haapakoski, R. (Rita), Lehto, M. (Maili), Desrumaux, C. (Catherine), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Mikkilä, V. (Vera), Hingorani, A. (Aroon), Humphries, S. E. (Steve E.), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Raitakari, O. (Olli), Ala-Korpela, M. (Mika), and Kivimäki, M. (MIka)
Subjects: Risk factors, Epidemiology, Predictive markers
Abstract: Diet may modify metabolomic profiles towards higher or lower cardiovascular disease (CVD) risk. We aimed to identify metabolite profiles associated with high adherence to dietary recommendations — the Alternative Healthy Eating Index (AHEI) — and the extent to which metabolites associated with AHEI also predict incident CVD. Relations between AHEI score and 80 circulating lipids and metabolites, quantified by nuclear magnetic resonance metabolomics, were examined using linear regression models in the Whitehall II study (n = 4824, 55.9 ± 6.1 years, 28.0% women) and were replicated in the Cardiovascular Risk in Young Finns Study (n = 1716, 37.7 ± 5.0 years, 56.3% women). We used Cox models to study associations between metabolites and incident CVD over the 15.8-year follow-up in the Whitehall II study. After adjustment for confounders, higher AHEI score (indicating healthier diet) was associated with higher degree of unsaturation of fatty acids (FA) and higher ratios of polyunsaturated FA, omega-3 and docosahexaenoic acid relative to total FA in both Whitehall II and Young Finns studies. A concordance of associations of metabolites with higher AHEI score and lower CVD risk was observed in Whitehall II. Adherence to healthy diet seems to be associated with specific FA that reduce risk of CVD.
Published: 2018

7. 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

Author: Tynkkynen, J. (Juho), Chouraki, V. (Vincent), Lee, S.J. (Sven) van der, Hernesniemi, J. (Jussi), Yang, Q. (Qiong), Li, S. (Shuo), Beiser, A. (Alexa), Larson, M.G. (Martin), Sääksjärvi, K. (K.), Shipley, M.J., Singh-Manoux, A. (Archana), Gerszten, R.E. (Robert E.), Wang, T.J. (Thomas J.), Havulinna, A.S. (Aki), Würtz, P. (Peter), Fischer, K. (Krista), Demirkan, A. (Ayşe), Ikram, M.K. (Kamran), Amin, N. (Najaf), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Perola, M. (Markus), Metspalu, A. (Andres), Kangas, A.J. (Antti), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Vasan, R. (Ramachandran), Kivimaki, M. (Mika), Duijn, C.M. (Cornelia) van, Seshadri, S. (Sudha), Salomaa, V. (Veikko), Tynkkynen, J. (Juho), Chouraki, V. (Vincent), Lee, S.J. (Sven) van der, Hernesniemi, J. (Jussi), Yang, Q. (Qiong), Li, S. (Shuo), Beiser, A. (Alexa), Larson, M.G. (Martin), Sääksjärvi, K. (K.), Shipley, M.J., Singh-Manoux, A. (Archana), Gerszten, R.E. (Robert E.), Wang, T.J. (Thomas J.), Havulinna, A.S. (Aki), Würtz, P. (Peter), Fischer, K. (Krista), Demirkan, A. (Ayşe), Ikram, M.K. (Kamran), Amin, N. (Najaf), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Perola, M. (Markus), Metspalu, A. (Andres), Kangas, A.J. (Antti), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Vasan, R. (Ramachandran), Kivimaki, M. (Mika), Duijn, C.M. (Cornelia) van, Seshadri, S. (Sudha), and Salomaa, V. (Veikko)
Abstract: 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 nu
Published: 2018
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8. Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study

Author: Teslovich, T. M. (Tanya M), Kim, D. S. (Daniel Seung), Yin, X. (Xianyong), Stančáková, A. (Alena), Jackson, A. U. (Anne U.), Wielscher, M. (Matthias), Naj, A. (Adam), Perry, J. R. (John R.B.), Huyghe, J. R. (Jeroen R.), Stringham, H. M. (Heather M.), Davis, J. P. (James P.), Raulerson, C. K. (Chelsea K.), Welch, R. P. (Ryan P.), Fuchsberger, C. (Christian), Locke, A. E. (Adam E.), Sim, X. (Xueling), Chines, P. S. (Peter S.), Narisu, N. (Narisu), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), G. o. (Genetics of Obesity-Related Liver Disease Consortium (GOLD)), T. A. (The Alzheimer’s Disease Genetics Consortium (ADGC)), T. D. (The DIAbetes Genetics Replication And Meta-analysis (DIAGRAM)), Ala-Korpela, M. (Mika), Gudnason, V. (Vilmundur), Musani, S. K. (Solomon K.), Järvelin, M.-R. (Marjo-Riitta), Schellenberg, G. D. (Gerard D.), Speliotes, E. K. (Elizabeth K.), Kuusisto, J. (Johanna), Collins, F. S. (Francis S.), Boehnke, M. (Michael), Laakso, M. (Markku), Mohlke, K. L. (Karen L.), Teslovich, T. M. (Tanya M), Kim, D. S. (Daniel Seung), Yin, X. (Xianyong), Stančáková, A. (Alena), Jackson, A. U. (Anne U.), Wielscher, M. (Matthias), Naj, A. (Adam), Perry, J. R. (John R.B.), Huyghe, J. R. (Jeroen R.), Stringham, H. M. (Heather M.), Davis, J. P. (James P.), Raulerson, C. K. (Chelsea K.), Welch, R. P. (Ryan P.), Fuchsberger, C. (Christian), Locke, A. E. (Adam E.), Sim, X. (Xueling), Chines, P. S. (Peter S.), Narisu, N. (Narisu), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), G. o. (Genetics of Obesity-Related Liver Disease Consortium (GOLD)), T. A. (The Alzheimer’s Disease Genetics Consortium (ADGC)), T. D. (The DIAbetes Genetics Replication And Meta-analysis (DIAGRAM)), Ala-Korpela, M. (Mika), Gudnason, V. (Vilmundur), Musani, S. K. (Solomon K.), Järvelin, M.-R. (Marjo-Riitta), Schellenberg, G. D. (Gerard D.), Speliotes, E. K. (Elizabeth K.), Kuusisto, J. (Johanna), Collins, F. S. (Francis S.), Boehnke, M. (Michael), Laakso, M. (Markku), and Mohlke, K. L. (Karen L.)
Abstract: Comprehensive metabolite profiling captures many highly heritable traits, including amino acid levels, which are potentially sensitive biomarkers for disease pathogenesis. To better understand the contribution of genetic variation to amino acid levels, we performed single variant and gene-based tests of association between nine serum amino acids (alanine, glutamine, glycine, histidine, isoleucine, leucine, phenylalanine, tyrosine, and valine) and 16.6 million genotyped and imputed variants in 8545 non-diabetic Finnish men from the METabolic Syndrome In Men (METSIM) study with replication in Northern Finland Birth Cohort (NFBC1966). We identified five novel loci associated with amino acid levels (P = < 5×10−8): LOC157273/PPP1R3B with glycine (rs9987289, P = 2.3×10−26); ZFHX3 (chr16:73326579, minor allele frequency (MAF) = 0.42%, P = 3.6×10−9), LIPC (rs10468017, P = 1.5×10−8), and WWOX (rs9937914, P = 3.8×10−8) with alanine; and TRIB1 with tyrosine (rs28601761, P = 8×10−9). Gene-based tests identified two novel genes harboring missense variants of MAF < 1% that show aggregate association with amino acid levels: PYCR1 with glycine (Pgene = 1.5×10−6) and BCAT2 with valine (Pgene = 7.4×10−7); neither gene was implicated by single variant association tests. These findings are among the first applications of gene-based tests to identify new loci for amino acid levels. In addition to the seven novel gene associations, we identified five independent signals at established amino acid loci, including two rare variant signals at GLDC (rs138640017, MAF=0.95%, Pconditional = 5.8×10−40) with glycine levels and HAL (rs141635447, MAF = 0.46%, Pconditional = 9.4×10−11) with histidine levels. Examination of all single variant association results in our data revealed a strong inverse relationship between effect size and MAF (Ptrend<0.001). These novel signals provide further insight into the molecular mechanisms of amino acid metabolism and potentially, their perturba
Published: 2018

9. NAFLD risk alleles in PNPLA3, TM6SF2, GCKR and LYPLAL1 show divergent metabolic effects

Author: Sliz, E. (Eeva), Sebert, S. (Sylvain), Würtz, P. (Peter), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Viikari, J. (Jorma), Männikkö, M. (Minna), Ala-Korpela, M. (Mika), Raitakari, O. T. (Olli T.), Kettunen, J. (Johannes), Sliz, E. (Eeva), Sebert, S. (Sylvain), Würtz, P. (Peter), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Viikari, J. (Jorma), Männikkö, M. (Minna), Ala-Korpela, M. (Mika), Raitakari, O. T. (Olli T.), and Kettunen, J. (Johannes)
Abstract: Fatty liver has been associated with unfavourable metabolic changes in circulation. To provide insights in fatty liver-related metabolic deviations, we compared metabolic association profile of fatty liver versus metabolic association profiles of genotypes increasing the risk of non-alcoholic fatty liver disease (NAFLD). The cross-sectional associations of ultrasound-ascertained fatty liver with 123 metabolic measures were determined in 1810 (Nfatty liver = 338) individuals aged 34–49 years from The Cardiovascular Risk in Young Finns Study. The association profiles of NAFLD-risk alleles in PNPLA3, TM6SF2, GCKR, and LYPLAL1 with the corresponding metabolic measures were obtained from a publicly available metabolomics GWAS including up to 24 925 Europeans. The risk alleles showed different metabolic effects: PNPLA3 rs738409-G, the strongest genetic NAFLD risk factor, did not associate with metabolic changes. Metabolic effects of GCKR rs1260326-T were comparable in many respects to the fatty liver associations. Metabolic effects of LYPLAL1 rs12137855-C were similar, but statistically less robust, to the effects of GCKR rs1260326-T. TM6SF2 rs58542926-T displayed opposite metabolic effects when compared with the fatty liver associations. The metabolic effects of the risk alleles highlight heterogeneity of the molecular pathways leading to fatty liver and suggest that the fatty liver-related changes in the circulating lipids and metabolites may vary depending on the underlying pathophysiological mechanism. Despite the robust cross-sectional associations on population level, the present results showing neutral or cardioprotective metabolic effects for some of the NAFLD risk alleles advocate that hepatic lipid accumulation by itself may not increase the level of circulating lipids or other metabolites.
Published: 2018

10. Effect of dietary counseling on a comprehensive metabolic profile from childhood to adulthood

Author: Lehtovirta, M. (Miia), Pahkala, K. (Katja), Niinikoski, H. (Harri), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Lagström, H. (Hanna), Viikari, J. S. (Jorma S. A.), Rönnemaa, T. (Tapani), Jula, A. (Antti), Ala-Korpela, M. (Mika), Würtz, P. (Peter), Raitakari, O. T. (Olli T.), Lehtovirta, M. (Miia), Pahkala, K. (Katja), Niinikoski, H. (Harri), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Lagström, H. (Hanna), Viikari, J. S. (Jorma S. A.), Rönnemaa, T. (Tapani), Jula, A. (Antti), Ala-Korpela, M. (Mika), Würtz, P. (Peter), and Raitakari, O. T. (Olli T.)
Abstract: Objectives: To study the effects of repeated, infancy-onset dietary counseling on a detailed metabolic profile. Effects of dietary saturated fat replacement on circulating concentrations of metabolic biomarkers still remain unknown. Study design: The Special Turku Coronary Risk Factor Intervention Project (STRIP) study is a longitudinal, randomized atherosclerosis prevention trial in which repeated dietary counseling aimed at reducing the proportion of saturated fat intake. Nuclear magnetic resonance metabolomics quantified circulating metabolites from serum samples assessed at age 9 (n = 554), 11 (n = 553), 13 (n = 508), 15 (n = 517), 17 (n = 457), and 19 (n = 417) years. Results: The intervention reduced dietary intake of saturated fat (mean difference in daily percentage of total energy intake: −2.1 [95% CI −1.9 to −2.3]) and increased intake of polyunsaturated fat (0.6 [0.5–0.7]). The dietary counseling intervention led to greater serum proportions of polyunsaturated fatty acids (P < .001), with greater proportions of both circulating omega-3 (P = .02) and omega-6 (P < .001) fatty acids. The proportion of saturated fatty acids in serum was lower for both boys and girls in the intervention group (P < .001), whereas the serum proportion of monounsaturated fat was lower for boys in the intervention group only (P < .001). The intervention also reduced circulating intermediate-density lipoprotein and low-density lipoprotein lipid concentrations (P < .01). Dietary intervention effects on nonlipid biomarkers were minor except from greater concentrations of glutamine in the intervention group. Conclusions: Repeated dietary counseling from infancy to early adulthood yielded favorable effects on multiple circulating fatty acids and lipoprotein subclass lipids, particularly in boys. These molecular effects substantiate the beneficial role of saturated fat replacement on the metabolic risk profile.
Published: 2018

11. Metabolic proﬁling of fatty liver in young and middle-aged adults:cross-sectional and prospective analyses of the young Finns study

Author: Kaikkonen, J. E. (Jari E.), Würtz, P. (Peter), Suomela, E. (Emmi), Lehtovirta, M. (Miia), Kangas, A. J. (Antti J.), Jula, A. (Antti), Mikkilä, V. (Vera), Viikari, J. S. (Jorma S.A.), Juonala, M. (Markus), Rönnemaa, T. (Tapani), Hutri-Kähönen, N. (Nina), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), and Raitakari, O. T. (Olli T.)
Abstract: Nonalcoholic fatty liver is associated with obesity-related metabolic disturbances, but little is known about the metabolic perturbations preceding fatty liver disease. We performed comprehensive metabolic profiling to assess how circulating metabolites, such as lipoprotein lipids, fatty acids, amino acids, and glycolysis-related metabolites, reflect the presence of and future risk for fatty liver in young adults. Sixty-eight lipids and metabolites were quantified by nuclear magnetic resonance metabolomics in the population-based Young Finns Study from serum collected in 2001 (n = 1,575), 2007 (n = 1,509), and 2011 (n = 2,002). Fatty liver was diagnosed by ultrasound in 2011 when participants were aged 34–49 years (19% prevalence). Cross-sectional associations as well as 4-year and 10-year risks for fatty liver were assessed by logistic regression. Metabolites across multiple pathways were strongly associated with the presence of fatty liver (P < 0.0007 for 60 measures in age-adjusted and sex-adjusted cross-sectional analyses). The strongest direct associations were observed for extremely large very-low-density lipoprotein triglycerides (odds ratio [OR] 5 4.86 per 1 standard deviation, 95% confidence interval 3.48–6.78), other very-low-density lipoprotein measures, and branched-chain amino acids (e.g., leucine OR = 2.94, 2.51–3.44). Strong inverse associations were observed for high-density lipoprotein measures, e.g., high-density lipoprotein size (OR = 0.36, 0.30–0.42) and several fatty acids including omega-6 (OR = 0.37, 0.32–0.42). The metabolic associations were attenuated but remained significant after adjusting for waist, physical activity, alcohol consumption, and smoking (P < 0.0007). Similar aberrations in the metabolic profile were observed already 10 years before fatty liver diagnosis. Conclusion: Circulating lipids, fatty acids, and amino acids reflect fatty liver independently of routine metabolic risk factors; these metabolic aberrations appear to precede the development of fatty liver in young adults.
Published: 2017

12. Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology:a primer on -omic technologies

Author: Würtz, P. (Peter), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Lawlor, D. A. (Debbie A.), Smith, G. D. (George Davey), and Ala-Korpela, M. (Mika)
Subjects: amino acids, nuclear magnetic resonance, Mendelian randomization, biomarkers, drug development, fatty acids, metabolomics, serum
Abstract: Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
Published: 2017

13. Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study

Author: Davis, J. P. (James P.), Huyghe, J. R. (Jeroen R.), Locke, A. E. (Adam E.), Jackson, A. U. (Anne U.), Sim, X. (Xueling), Stringham, H. M. (Heather M.), Teslovich, T. M. (Tanya M.), Welch, R. P. (Ryan P.), Fuchsberger, C. (Christian), Narisu, N. (Narisu), Chines, P. S. (Peter S.), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Kuusisto, J. (Johanna), Collins, F. S. (Francis S.), Laakso, M. (Markku), Boehnke, M. (Michael), and Mohlke, K. L. (Karen L.)
Abstract: Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at HIF3A, ADAMTS3, PLTP, LCAT, and LIPG. Four of the signals were identified with a low-frequency (0.005
Published: 2017

14. The biomarker and causal roles of homoarginine in the development of cardiometabolic diseases:an observational and Mendelian randomization analysis

Author: I. S. (Ilkka Seppälä), Oksala, N. (Niku), Jula, A. (Antti), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Hutri-Kähönen, N. (Nina), März, W. (Winfried), Meinitzer, A. (Andreas), Juonala, M. (Markus), Kähönen, M. (Mika), Raitakari, O. T. (Olli T.), and Lehtimäki, T. (Terho)
Subjects: Risk factors, Epidemiology, Predictive markers
Abstract: High L-homoarginine (hArg) levels are directly associated with several risk factors for cardiometabolic diseases whereas low levels predict increased mortality in prospective studies. The biomarker role of hArg in young adults remains unknown. To study the predictive value of hArg in the development of cardiometabolic risk factors and diseases, we utilized data on high-pressure liquid chromatography-measured hArg, cardiovascular risk factors, ultrasound markers of preclinical atherosclerosis and type 2 diabetes from the population-based Young Finns Study involving 2,106 young adults (54.6% females, aged 24–39). We used a Mendelian randomization approach involving tens to hundreds of thousands of individuals to test causal associations. In our 10-year follow-up analysis, hArg served as an independent predictor for future hyperglycaemia (OR 1.31, 95% CI 1.06–1.63) and abdominal obesity (OR 1.60, 95% 1.14–2.30) in men and type 2 diabetes in women (OR 1.55, 95% CI 1.02–2.41). The MR analysis revealed no evidence of causal associations between serum hArg and any of the studied cardiometabolic outcomes. In conclusion, lifetime exposure to higher levels of circulating hArg does not seem to alter cardiometabolic disease risk. Whether hArg could be used as a biomarker for identification of individuals at risk developing cardiometabolic abnormalities merits further investigation.
Published: 2017

15. Impaired HDL2-mediated cholesterol efflux is associated with metabolic syndrome in families with early onset coronary heart disease and low HDL-cholesterol level

Author: Paavola, T. (Timo), Kuusisto, S. (Sanna), Jauhiainen, M. (Matti), Kakko, S. (Sakari), Kangas-Kontio, T. (Tiia), Metso, J. (Jari), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Bloigu, R. (Risto), Hannuksela, M. L. (Minna L.), Savolainen, M. J. (Markku J.), Salonurmi, T. (Tuire), Paavola, T. (Timo), Kuusisto, S. (Sanna), Jauhiainen, M. (Matti), Kakko, S. (Sakari), Kangas-Kontio, T. (Tiia), Metso, J. (Jari), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Bloigu, R. (Risto), Hannuksela, M. L. (Minna L.), Savolainen, M. J. (Markku J.), and Salonurmi, T. (Tuire)
Abstract: Objective: The potential of high-density lipoproteins (HDL) to facilitate cholesterol removal from arterial foam cells is a key function of HDL. We studied whether cholesterol efflux to serum and HDL subfractions is impaired in subjects with early coronary heart disease (CHD) or metabolic syndrome (MetS) in families where a low HDL-cholesterol level (HDL-C) predisposes to early CHD. Methods: HDL subfractions were isolated from plasma by sequential ultracentrifugation. THP-1 macrophages loaded with acetyl-LDL were used in the assay of cholesterol efflux to total HDL, HDL2, HDL3 or serum. Results: While cholesterol efflux to serum, total HDL and HDL3 was unchanged, the efflux to HDL2 was 14% lower in subjects with MetS than in subjects without MetS (p<0.001). The efflux to HDL2 was associated with components of MetS such as plasma HDL-C (r = 0.76 in men and r = 0.56 in women, p<0.001 for both). The efflux to HDL2 was reduced in men with early CHD (p<0.01) only in conjunction with their low HDL-C. The phospholipid content of HDL2 particles was a major correlate with the efflux to HDL2 (r = 0.70, p<0.001). A low ratio of HDL2 to total HDL was associated with MetS (p<0.001). Conclusion: Our results indicate that impaired efflux to HDL2 is a functional feature of the low HDL-C state and MetS in families where these risk factors predispose to early CHD. The efflux to HDL2 related to the phospholipid content of HDL2 particles but the phospholipid content did not account for the impaired efflux in cardiometabolic disease, where a combination of low level and poor quality of HDL2 was observed.
Published: 2017

16. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity

Author: Wahl, S. (Simone), Drong, A. (Alexander), Lehne, B. (Benjamin), Loh, M. (Marie), Scott, W.R. (William R.), Kunze, S. (Sonja), Tsai, P.-C. (Pei-Chien), Ried, J.S. (Janina S.), Zhang, W. (Weihua), Yang, Y. (Youwen), Tan, S. (Sili), Fiorito, G. (Giovanni), Franke, L. (Lude), Guarrera, S. (Simonetta), Kasela, S. (Silva), Kriebel, J. (Jennifer), Richmond, R.C. (Rebecca C.), Adamo, M. (Marco), Afzal, U. (Uzma), Ala-Korpela, M. (Mika), Albetti, B. (Benedetta), Ammerpohl, O. (Ole), Apperley, J. (Jane), Beekman, M. (Marian), Bertazzi, P.A. (Pier Alberto), Black, S.L. (S. Lucas), Blancher, C. (Christine), Bonder, M.J. (Marc), Brosch, M. (Mario), Carstensen-Kirberg, M. (Maren), Craen, A.J. (Anton) de, Lusignan, S. (Simon) de, Dehghan, A. (Abbas), Elkalaawy, M. (Mohamed), Fischer, K. (Krista), Franco, O.H. (Oscar), Gaunt, T.R. (Tom), Hampe, J. (Jochen), Hashemi, M. (Majid), Isaacs, A.J. (Aaron), Jenkinson, A. (Andrew), Jha, S. (Sujeet), Kato, N. (Norihiro), Krogh, V. (Vittorio), Laffan, M. (Michael), Meisinger, C. (Christa), Meitinger, T. (Thomas), Mok, Z.Y. (Zuan Yu), Motta, V. (Valeria), Ng, H.K. (Hong Kiat), Nikolakopoulou, Z. (Zacharoula), Nteliopoulos, G. (Georgios), Panico, S. (Salvatore), Pervjakova, N. (Natalia), Prokisch, H. (Holger), Rathmann, W. (Wolfgang), Roden, M. (Michael), Rota, F. (Federica), Rozario, M.A. (Michelle Ann), Sandling, J.K. (Johanna), Schafmayer, C. (Clemens), Schramm, K. (Katharina), Siebert, R. (Reiner), Slagboom, P.E. (Eline), Soininen, P. (Pasi), Stolk, L. (Lisette), Strauch, K. (Konstantin), Tai, E.S. (Shyong), Tarantini, L. (Letizia), Thorand, B. (Barbara), Tigchelaar, E.F. (Ettje F.), Tumino, R. (Rosario), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Meurs, J.B.J. (Joyce) van, Vineis, P. (Paolo), Wickremasinghe, A.R. (Ananda), Wijmenga, C. (Cisca), Yang, T.-P. (Tsun-Po), Yuan, W. (Wei), Zhernakova, A. (Alexandra), Batterham, R.L., Smith, A.V. (Davey), Deloukas, P. (Panos), Heijmans, B.T. (Bastiaan T.), Herder, C. (Christian), Hofman, A. (Albert), Lindgren, C.M. (Cecilia M.), Milani, L. (Lili), Harst, P. (Pim) van der, Peters, A. (Annette), Illig, T. (Thomas), Relton, C.L. (Caroline), Waldenberger, M. (Melanie), Jarvelin, M.-R. (Marjo-Riitta), Bollati, V. (Valentina), Soong, R. (Richie), Spector, T.D. (Timothy), Scott, J. (James), McCarthy, M.I. (Mark), Elliott, P. (Paul), Bell, J.T. (Jordana T.), Matullo, G., Gieger, C. (Christian), Kooner, J.S. (Jaspal S.), Grallert, H. (Harald), Chambers, J.C. (John C.), Wahl, S. (Simone), Drong, A. (Alexander), Lehne, B. (Benjamin), Loh, M. (Marie), Scott, W.R. (William R.), Kunze, S. (Sonja), Tsai, P.-C. (Pei-Chien), Ried, J.S. (Janina S.), Zhang, W. (Weihua), Yang, Y. (Youwen), Tan, S. (Sili), Fiorito, G. (Giovanni), Franke, L. (Lude), Guarrera, S. (Simonetta), Kasela, S. (Silva), Kriebel, J. (Jennifer), Richmond, R.C. (Rebecca C.), Adamo, M. (Marco), Afzal, U. (Uzma), Ala-Korpela, M. (Mika), Albetti, B. (Benedetta), Ammerpohl, O. (Ole), Apperley, J. (Jane), Beekman, M. (Marian), Bertazzi, P.A. (Pier Alberto), Black, S.L. (S. Lucas), Blancher, C. (Christine), Bonder, M.J. (Marc), Brosch, M. (Mario), Carstensen-Kirberg, M. (Maren), Craen, A.J. (Anton) de, Lusignan, S. (Simon) de, Dehghan, A. (Abbas), Elkalaawy, M. (Mohamed), Fischer, K. (Krista), Franco, O.H. (Oscar), Gaunt, T.R. (Tom), Hampe, J. (Jochen), Hashemi, M. (Majid), Isaacs, A.J. (Aaron), Jenkinson, A. (Andrew), Jha, S. (Sujeet), Kato, N. (Norihiro), Krogh, V. (Vittorio), Laffan, M. (Michael), Meisinger, C. (Christa), Meitinger, T. (Thomas), Mok, Z.Y. (Zuan Yu), Motta, V. (Valeria), Ng, H.K. (Hong Kiat), Nikolakopoulou, Z. (Zacharoula), Nteliopoulos, G. (Georgios), Panico, S. (Salvatore), Pervjakova, N. (Natalia), Prokisch, H. (Holger), Rathmann, W. (Wolfgang), Roden, M. (Michael), Rota, F. (Federica), Rozario, M.A. (Michelle Ann), Sandling, J.K. (Johanna), Schafmayer, C. (Clemens), Schramm, K. (Katharina), Siebert, R. (Reiner), Slagboom, P.E. (Eline), Soininen, P. (Pasi), Stolk, L. (Lisette), Strauch, K. (Konstantin), Tai, E.S. (Shyong), Tarantini, L. (Letizia), Thorand, B. (Barbara), Tigchelaar, E.F. (Ettje F.), Tumino, R. (Rosario), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Meurs, J.B.J. (Joyce) van, Vineis, P. (Paolo), Wickremasinghe, A.R. (Ananda), Wijmenga, C. (Cisca), Yang, T.-P. (Tsun-Po), Yuan, W. (Wei), Zhernakova, A. (Alexandra), Batterham, R.L., Smith, A.V. (Davey), Deloukas, P. (Panos), Heijmans, B.T. (Bastiaan T.), Herder, C. (Christian), Hofman, A. (Albert), Lindgren, C.M. (Cecilia M.), Milani, L. (Lili), Harst, P. (Pim) van der, Peters, A. (Annette), Illig, T. (Thomas), Relton, C.L. (Caroline), Waldenberger, M. (Melanie), Jarvelin, M.-R. (Marjo-Riitta), Bollati, V. (Valentina), Soong, R. (Richie), Spector, T.D. (Timothy), Scott, J. (James), McCarthy, M.I. (Mark), Elliott, P. (Paul), Bell, J.T. (Jordana T.), Matullo, G., Gieger, C. (Christian), Kooner, J.S. (Jaspal S.), Grallert, H. (Harald), and Chambers, J.C. (John C.)
Abstract: Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10 -7, range P = 9.2 × 10 -8 to 6.0 × 10 -46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10 -6, range P = 5.5 × 10 -6 to 6.1 × 10 -35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 × 10 -54). Our results provide new insights into the biologic pathways influenc
Published: 2017
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17. Experimental and human evidence for lipocalin-2 (Neutrophil Gelatinase-Associated Lipocalin [NGAL]) in the development of cardiac hypertrophy and heart failure

Author: Marques, F. Z. (Francine Z.), Prestes, P. R. (Priscilla R.), Byars, S. G. (Sean G.), Ritchie, S. C. (Scott C.), Würtz, P. (Peter), Patel, S. K. (Sheila K.), Booth, S. A. (Scott A.), Rana, I. (Indrajeetsinh), Minoda, Y. (Yosuke), Berzins, S. P. (Stuart P.), Curl, C. L. (Claire L.), Bell, J. R. (James R.), Wai, B. (Bryan), Srivastava, P. M. (Piyush M.), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ruohonen, S. (Saku), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Raitoharju, E. (Emma), Havulinna, A. (Aki), Perola, M. (Markus), Raitakari, O. (Olli), Salomaa, V. (Veikko), Ala‐Korpela, M. (Mika), Kettunen, J. (Johannes), McGlynn, M. (Maree), Kelly, J. (Jason), Wlodek, M. E. (Mary E.), Lewandowski, P. A. (Paul A.), Delbridge, L. M. (Lea M.), Burrell, L. M. (Louise M.), Inouye, M. (Michael), Harrap, S. B. (Stephen B.), Charchar, F. J. (Fadi J.), Marques, F. Z. (Francine Z.), Prestes, P. R. (Priscilla R.), Byars, S. G. (Sean G.), Ritchie, S. C. (Scott C.), Würtz, P. (Peter), Patel, S. K. (Sheila K.), Booth, S. A. (Scott A.), Rana, I. (Indrajeetsinh), Minoda, Y. (Yosuke), Berzins, S. P. (Stuart P.), Curl, C. L. (Claire L.), Bell, J. R. (James R.), Wai, B. (Bryan), Srivastava, P. M. (Piyush M.), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ruohonen, S. (Saku), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Raitoharju, E. (Emma), Havulinna, A. (Aki), Perola, M. (Markus), Raitakari, O. (Olli), Salomaa, V. (Veikko), Ala‐Korpela, M. (Mika), Kettunen, J. (Johannes), McGlynn, M. (Maree), Kelly, J. (Jason), Wlodek, M. E. (Mary E.), Lewandowski, P. A. (Paul A.), Delbridge, L. M. (Lea M.), Burrell, L. M. (Louise M.), Inouye, M. (Michael), Harrap, S. B. (Stephen B.), and Charchar, F. J. (Fadi J.)
Abstract: Background: Cardiac hypertrophy increases the risk of developing heart failure and cardiovascular death. The neutrophil inflammatory protein, lipocalin‐2 (LCN2/NGAL), is elevated in certain forms of cardiac hypertrophy and acute heart failure. However, a specific role for LCN2 in predisposition and etiology of hypertrophy and the relevant genetic determinants are unclear. Here, we defined the role of LCN2 in concentric cardiac hypertrophy in terms of pathophysiology, inflammatory expression networks, and genomic determinants. Methods and Results: We used 3 experimental models: a polygenic model of cardiac hypertrophy and heart failure, a model of intrauterine growth restriction and Lcn2‐knockout mouse; cultured cardiomyocytes; and 2 human cohorts: 114 type 2 diabetes mellitus patients and 2064 healthy subjects of the YFS (Young Finns Study). In hypertrophic heart rats, cardiac and circulating Lcn2 was significantly overexpressed before, during, and after development of cardiac hypertrophy and heart failure. Lcn2 expression was increased in hypertrophic hearts in a model of intrauterine growth restriction, whereas Lcn2‐knockout mice had smaller hearts. In cultured cardiomyocytes, Lcn2 activated molecular hypertrophic pathways and increased cell size, but reduced proliferation and cell numbers. Increased LCN2 was associated with cardiac hypertrophy and diastolic dysfunction in diabetes mellitus. In the YFS,LCN2 expression was associated with body mass index and cardiac mass and with levels of inflammatory markers. The single‐nucleotide polymorphism, rs13297295, located near LCN2 defined a significant cis‐eQTL for LCN2 expression. Conclusions: Direct effects of LCN2 on cardiomyocyte size and number and the consistent associations in experimental and human analyses reveal a central role for LCN2 in the ontogeny of cardiac hypertrophy and heart failure.
Published: 2017

18. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort

Author: Org, E. (Elin), Blum, Y. (Yuna), Kasela, S. (Silva), Mehrabian, M. (Margarete), Kuusisto, J. (Johanna), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Wang, Z. (Zeneng), Ala-Korpela, M. (Mika), Hazen, S. L. (Stanley L.), Laakso, M. (Markku), Lusis, A. J. (Aldons J.), Org, E. (Elin), Blum, Y. (Yuna), Kasela, S. (Silva), Mehrabian, M. (Margarete), Kuusisto, J. (Johanna), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Wang, Z. (Zeneng), Ala-Korpela, M. (Mika), Hazen, S. L. (Stanley L.), Laakso, M. (Markku), and Lusis, A. J. (Aldons J.)
Abstract: Background: The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study. Results: We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota–metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state. Conclusions: Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host–gut microbiota relationships.
Published: 2017

19. Trans-ancestry fine mapping and molecular assays identify regulatory variants at the ANGPTL8 HDL-C GWAS locus

Author: Cannon, M. E. (Maren E.), Duan, Q. (Qing), Wu, Y. (Ying), Zeynalzadeh, M. (Monica), Xu, Z. (Zheng), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Civelek, M. (Mete), Lusis, A. J. (Aldons J.), Kuusisto, J. (Johanna), Collins, F. S. (Francis S.), Boehnke, M. (Michael), Tang, H. (Hua), Laakso, M. (Markku), Li, Y. (Yun), Mohlke, K. L. (Karen L.), Cannon, M. E. (Maren E.), Duan, Q. (Qing), Wu, Y. (Ying), Zeynalzadeh, M. (Monica), Xu, Z. (Zheng), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Civelek, M. (Mete), Lusis, A. J. (Aldons J.), Kuusisto, J. (Johanna), Collins, F. S. (Francis S.), Boehnke, M. (Michael), Tang, H. (Hua), Laakso, M. (Markku), Li, Y. (Yun), and Mohlke, K. L. (Karen L.)
Abstract: Recent genome-wide association studies (GWAS) have identified variants associated with high-density lipoprotein cholesterol (HDL-C) located in or near the ANGPTL8 gene. Given the extensive sharing of GWAS loci across populations, we hypothesized that at least one shared variant at this locus affects HDL-C. The HDL-C–associated variants are coincident with expression quantitative trait loci for ANGPTL8 and DOCK6 in subcutaneous adipose tissue; however, only ANGPTL8 expression levels are associated with HDL-C levels. We identified a 400-bp promoter region of ANGPTL8 and enhancer regions within 5 kb that contribute to regulating expression in liver and adipose. To identify variants functionally responsible for the HDL-C association, we performed fine-mapping analyses and selected 13 candidate variants that overlap putative regulatory regions to test for allelic differences in regulatory function. Of these variants, rs12463177-G increased transcriptional activity (1.5-fold, P = 0.004) and showed differential protein binding. Six additional variants (rs17699089, rs200788077, rs56322906, rs3760782, rs737337, and rs3745683) showed evidence of allelic differences in transcriptional activity and/or protein binding. Taken together, these data suggest a regulatory mechanism at the ANGPTL8 HDL-C GWAS locus involving tissue-selective expression and at least one functional variant.
Published: 2017

20. Differential associations of inflammatory markers with insulin sensitivity and secretion:the prospective METSIM study

Author: Fizelova, M. (Maria), Jauhiainen, R. (Raimo), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Kuusisto, J. (Johanna), Laakso, M. (Markku), Stančáková, A. (Alena), Fizelova, M. (Maria), Jauhiainen, R. (Raimo), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Kuusisto, J. (Johanna), Laakso, M. (Markku), and Stančáková, A. (Alena)
Abstract: Context: Low-grade inflammation is involved in the development of type 2 diabetes and cardiovascular disease (CVD); however, prospective studies evaluating inflammatory markers as predictors of changes in insulin secretion and insulin sensitivity are lacking. Objective: We investigated the associations of glycoprotein acetyls (GlycA), interleukin-1 receptor antagonist (IL-1RA), and high-sensitivity C-reactive protein (hs-CRP) with insulin secretion, insulin sensitivity, incident type 2 diabetes, hypertension, CVD events, and total mortality in the prospective Metabolic Syndrome in Men (METSIM) study. Design: A prospective study. Participants: The cross-sectional METSIM study included 8749 nondiabetic Finnish men aged 45 to 73 years, who had been randomly selected from the population register of Kuopio, Finland. A total of 5401 men participated in the 6.8-year follow-up study. Main Outcome Measures: Changes in insulin secretion, insulin sensitivity, and cardiometabolic traits during the follow-up period and the incidence of type 2 diabetes, hypertension, CVD events, and total mortality. Results: During the follow-up period, GlycA was associated with impaired insulin secretion, hyperglycemia, incident type 2 diabetes (hazard ratio, 1.37; 95% confidence interval, 1.29 to 1.46) and CVD (hazard ratio, 1.21; 95% confidence interval, 1.12 to 1.32). IL-1RA and hs-CRP were associated with adverse changes in insulin sensitivity and obesity-related traits and with total mortality (hazard ratio, 1.13; 95% confidence interval, 1.07 to 1.20; and hazard ratio, 1.08; 95% confidence interval, 1.04 to 1.11, respectively). Conclusions: Inflammatory markers differentially predicted changes in insulin secretion and insulin sensitivity. GlycA predicted impaired insulin secretion, and IL-1RA and hs-CRP predicted changes in insulin sensitivity. Combining the three markers improved the prediction of disease outcomes, suggesting that they capture different aspects of low-grade infla
Published: 2017

21. Association of pre-pregnancy body mass index with offspring metabolic profile:analyses of 3 European prospective birth cohorts

Author: Santos Ferreira, D. L. (Diana L.), Williams, D. M. (Dylan M.), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Smith, G. D. (George Davey), Järvelin, M.-R. (Marjo-Riitta), Lawlor, D. A. (Debbie A.), Santos Ferreira, D. L. (Diana L.), Williams, D. M. (Dylan M.), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Ala-Korpela, M. (Mika), Smith, G. D. (George Davey), Järvelin, M.-R. (Marjo-Riitta), and Lawlor, D. A. (Debbie A.)
Abstract: Background: A high proportion of women start pregnancy overweight or obese. According to the developmental overnutrition hypothesis, this could lead offspring to have metabolic disruption throughout their lives and thus perpetuate the obesity epidemic across generations. Concerns about this hypothesis are influencing antenatal care. However, it is unknown whether maternal pregnancy adiposity is associated with long-term risk of adverse metabolic profiles in offspring, and if so, whether this association is causal, via intrauterine mechanisms, or explained by shared familial (genetic, lifestyle, socioeconomic) characteristics. We aimed to determine if associations between maternal body mass index (BMI) and offspring systemic cardio-metabolic profile are causal, via intrauterine mechanisms, or due to shared familial factors. Methods and findings: We used 1- and 2-stage individual participant data (IPD) meta-analysis, and a negative-control (paternal BMI) to examine the association between maternal pre-pregnancy BMI and offspring serum metabolome from 3 European birth cohorts (offspring age at blood collection: 16, 17, and 31 years). Circulating metabolic traits were quantified by high-throughput nuclear magnetic resonance metabolomics. Results from 1-stage IPD meta-analysis (N = 5327 to 5377 mother-father-offspring trios) showed that increasing maternal and paternal BMI was associated with an adverse cardio-metabolic profile in offspring. We observed strong positive associations with very-low-density lipoprotein (VLDL)-lipoproteins, VLDL-cholesterol (C), VLDL-triglycerides, VLDL-diameter, branched/aromatic amino acids, glycoprotein acetyls, and triglycerides, and strong negative associations with high-density lipoprotein (HDL), HDL-diameter, HDL-C, HDL₂-C, and HDL₃-C (all P < 0.003). Slightly stronger magnitudes of associations were present for maternal compared with paternal BMI across these associations; however, there was no strong statistical evide
Published: 2017

22. An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

Author: Nath, A. P. (Artika P.), Ritchie, S. C. (Scott C.), Byars, S. G. (Sean G.), Fearnley, L. G. (Liam G.), Havulinna, A. S. (Aki S.), Joensuu, A. (Anni), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Wennerström, A. (Annika), Milani, L. (Lili), Metspalu, A. (Andres), Männistö, S. (Satu), Würtz, P. (Peter), Kettunen, J. (Johannes), Raitoharju, E. (Emma), Kähönen, M. (Mika), Juonala, M. (Markus), Palotie, A. (Aarno), Ala-Korpela, M. (Mika), Ripatti, S. (Samuli), Lehtimäki, T. (Terho), Abraham, G. (Gad), Raitakari, O. (Olli), Salomaa, V. (Veikko), Perola, M. (Markus), Inouye, M. (Michael), Nath, A. P. (Artika P.), Ritchie, S. C. (Scott C.), Byars, S. G. (Sean G.), Fearnley, L. G. (Liam G.), Havulinna, A. S. (Aki S.), Joensuu, A. (Anni), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Wennerström, A. (Annika), Milani, L. (Lili), Metspalu, A. (Andres), Männistö, S. (Satu), Würtz, P. (Peter), Kettunen, J. (Johannes), Raitoharju, E. (Emma), Kähönen, M. (Mika), Juonala, M. (Markus), Palotie, A. (Aarno), Ala-Korpela, M. (Mika), Ripatti, S. (Samuli), Lehtimäki, T. (Terho), Abraham, G. (Gad), Raitakari, O. (Olli), Salomaa, V. (Veikko), Perola, M. (Markus), and Inouye, M. (Michael)
Abstract: Background: Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up. Results: We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus. Conclusions: This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.
Published: 2017

23. Epidemiological applications of quantitative serum NMR metabolomics:causal inference from observational studies

Author: Kettunen, J. (Johannes), Würtz, P. (Peter), Soininen, P. (Pasi), Wang, Q. (Qin), Kettunen, J. (Johannes), Würtz, P. (Peter), Soininen, P. (Pasi), and Wang, Q. (Qin)
Abstract: Cardiovascular diseases are the leading cause of death worldwide and type 2 diabetes is reaching a global epidemic. Epidemiological studies have identified numerous risk factors and pharmacotherapies in relation to these cardiometabolic diseases. However, the detailed molecular mechanisms of these risk factors and drug therapies generally remain incompletely understood. Elucidating the underlying molecular effects would be essential for better understanding of the disease pathogenesis and also for discovering new therapeutic targets. Quantitative serum metabolomics, which allows for simultaneous quantification of multiple circulating metabolic measures, provides a hypothesis-free approach to systematically inspect the metabolic changes in response to endogenous and exogenous stimuli. Metabolomics thus presents a valuable tool to study the detailed molecular effects of disease risk factors and drug therapies. However, current metabolomics studies are mostly conducted in small cross-sectional studies and the causal relations of the risk factors on the metabolic measures are generally unclear, providing limited public health impact. The present thesis serves as a proof-of-concept to illustrate that well-designed observational studies can be used to infer causality. With the exemplars of assessing molecular effects of two risk factors (body mass index and sex hormone-binding globulin) and two drug therapies (statins and oral contraceptives), the thesis demonstrates that an improved causal inference can be achieved in observational studies via the combination of multiple study designs, including cross-sectional, longitudinal and Mendelian randomization analysis. This robust study design approach together with metabolomics data can be also extended to study the molecular effects of other risk factors and drug therapies. With an improved molecular understanding of a wide range of risk factors and therapies, better understanding of disease pathogenesis is ensured., Tiivistelmä Sydän- ja verisuonitaudit ovat johtava kuolinsyy maailmassa ja tyypin 2 diabetes on saavuttamassa globaalin epidemian mittasuhteet. Epidemiologiset tutkimukset ovat löytäneet useita riskitekijöitä ja lääkehoitoja edellä mainituille yleisille taudeille. Tyypin 2 diabetekseen ja sydän- ja verisuonitauteihin liittyvät yksityiskohtaiset molekylaariset mekanismit ymmärretään kuitenkin puutteellisesti. Molekylaaristen yksityiskohtien tarkempi ymmärtäminen olisi siten erittäin merkittävää sekä tautiprosessien ymmärtämiseksi että lääkehoitojen kehittämiseksi. Seerumin kvantitatiivinen metabolomiikka mahdollistaa useiden metabolisten suureiden samanaikaisen määrittämisen verenkierrosta ja tarjoaa siten hypoteesittoman lähestymistavan sekä sisäisten että ulkoisten ärsykkeiden aiheuttamien metabolisten muutosten systemaattiseen tutkimukseen. Metabolomiikka on siten arvokas työkalu yksityiskohtaisten molekylaaristen mekanismien tutkimuksessa, olipa kyseessä taudin riskitekijät tai lääkehoito. Metabolomiikkatutkimuksia on kuitenkin pääasiassa tehty pienissä poikittaistutkimuksissa ja riskitekijöihin liittyvien metabolisten suureiden syy- ja seuraussuhteet ovat yleisesti epäselviä, josta johtuen metabolisten suureiden kansanterveydellinen sovellettavuus on ollut heikkoa. Tämä väitöskirja esittelee tutkimuskonseptin hyvin suunniteltujen havaintotutkimuksien soveltamiseksi syy- ja seuraussuhteiden arvioinnissa. Työ sisältää esimerkit kahden riskitekijän (painoindeksi ja sukupuolihormoneja sitova globuliini) ja kahden lääkehoidon (statiinit ja ehkäisypillerit) molekylaaristen vaikutusten kausaalisista tutkimuksista. Tulokset havainnollistavat, että kausaalisten johtopäätösten luotettavuutta voidaan parantaa yhdistämällä useita tutkimusasetelmia, kuten poikittais- ja pitkittäistutkimuksia sekä Mendelististä satunnaistamista. Esitettyjä luotettavia tutkimusasetelmia, yhdessä metabolomiikkadatan kanssa, voidaan laajentaa muiden riskitekijöiden ja lääkehoitojen molekylaaris
Published: 2017

24. Metabolic profiling of pregnancy:cross-sectional and longitudinal evidence

Author: Wang, Q. (Qin), Würtz, P. (Peter), Auro, K. (Kirsi), Mäkinen, V.-P. (Ville-Petteri), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Tiainen, M. (Mika), Tynkkynen, T. (Tuulia), Jokelainen, J. (Jari), Santalahti, K. (Kristiina), Salmi, M. (Marko), Blankenberg, S. (Stefan), Zeller, T. (Tanja), Viikari, J. (Jorma), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Salomaa, V. (Veikko), Perola, M. (Markus), Jalkanen, S. (Sirpa), Järvelin, M.-R. (Marjo-Riitta), Raitakari, O. T. (Olli T.), Kettunen, J. (Johannes), Lawlor, D. A. (Debbie A.), Ala-Korpela, M. (Mika), Wang, Q. (Qin), Würtz, P. (Peter), Auro, K. (Kirsi), Mäkinen, V.-P. (Ville-Petteri), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Tiainen, M. (Mika), Tynkkynen, T. (Tuulia), Jokelainen, J. (Jari), Santalahti, K. (Kristiina), Salmi, M. (Marko), Blankenberg, S. (Stefan), Zeller, T. (Tanja), Viikari, J. (Jorma), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Salomaa, V. (Veikko), Perola, M. (Markus), Jalkanen, S. (Sirpa), Järvelin, M.-R. (Marjo-Riitta), Raitakari, O. T. (Olli T.), Kettunen, J. (Johannes), Lawlor, D. A. (Debbie A.), and Ala-Korpela, M. (Mika)
Abstract: Background: Pregnancy triggers well-known alterations in maternal glucose and lipid balance but its overall effects on systemic metabolism remain incompletely understood. Methods: Detailed molecular profiles (87 metabolic measures and 37 cytokines) were measured for up to 4260 women (24–49 years, 322 pregnant) from three population-based cohorts in Finland. Circulating molecular concentrations in pregnant women were compared to those in non-pregnant women. Metabolic profiles were also reassessed for 583 women 6 years later to uncover the longitudinal metabolic changes in response to change in the pregnancy status. Results: Compared to non-pregnant women, all lipoprotein subclasses and lipids were markedly increased in pregnant women. The most pronounced differences were observed for the intermediate-density, low-density and high-density lipoprotein triglyceride concentrations. Large differences were also seen for many fatty acids and amino acids. Pregnant women also had higher concentrations of low-grade inflammatory marker glycoprotein acetyls, higher concentrations of interleukin-18 and lower concentrations of interleukin-12p70. The changes in metabolic concentrations for women who were not pregnant at baseline but pregnant 6 years later (or vice versa) matched (or were mirror-images of) the cross-sectional association pattern. Cross-sectional results were consistent across the three cohorts and similar longitudinal changes were seen for 653 women in 4-year and 497 women in 10-year follow-up. For multiple metabolic measures, the changes increased in magnitude across the three trimesters. Conclusions: Pregnancy initiates substantial metabolic and inflammatory changes in the mothers. Comprehensive characterisation of normal pregnancy is important for gaining understanding of the key nutrients for fetal growth and development. These findings also provide a valuable molecular reference in relation to studies of adverse pregnancy outcomes.
Published: 2016

25. Metabolic signatures of birthweight in 18 288 adolescents and adults

Author: Würtz, P. (Peter), Wang, Q. (Qin), Niironen, M. (Marjo), Tynkkynen, T. (Tuulia), Tiainen, M. (Mika), Drenos, F. (Fotios), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Skilton, M. R. (Michael R.), Heikkilä, K. (Kauko), Pouta, A. (Anneli), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Rose, R. J. (Richard J.), Kajantie, E. (Eero), Perola, M. (Markus), Kaprio, J. (Jaakko), Eriksson, J. G. (Johan G.), Raitakari, O. T. (Olli T.), Lawlor, D. A. (Debbie A.), Smith, G. D. (George Davey), Järvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), Auro, K. (Kirsi), Würtz, P. (Peter), Wang, Q. (Qin), Niironen, M. (Marjo), Tynkkynen, T. (Tuulia), Tiainen, M. (Mika), Drenos, F. (Fotios), Kangas, A. J. (Antti J.), Soininen, P. (Pasi), Skilton, M. R. (Michael R.), Heikkilä, K. (Kauko), Pouta, A. (Anneli), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Rose, R. J. (Richard J.), Kajantie, E. (Eero), Perola, M. (Markus), Kaprio, J. (Jaakko), Eriksson, J. G. (Johan G.), Raitakari, O. T. (Olli T.), Lawlor, D. A. (Debbie A.), Smith, G. D. (George Davey), Järvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), and Auro, K. (Kirsi)
Abstract: Background: Lower birthweight is associated with increased susceptibility to cardiometabolic diseases in adulthood, but the underlying molecular pathways are incompletely understood. We examined associations of birthweight with a comprehensive metabolic profile measured in adolescents and adults. Methods: High-throughput nuclear magnetic resonance metabolomics and biochemical assays were used to quantify 87 circulating metabolic measures in seven cohorts from Finland and the UK, comprising altogether 18 288 individuals (mean age 26 years, range 15–75). Metabolic associations with birthweight were assessed by linear regression models adjusted for sex, gestational age and age at blood sampling. The metabolic associations with birthweight were compared with the corresponding associations with adult body mass index (BMI). Results: Lower birthweight adjusted for gestational age was adversely associated with cardiometabolic biomarkers, including lipoprotein subclasses, fatty acids, amino acids and markers of inflammation and impaired liver function (P < 0.0015 for 46 measures). Associations were consistent across cohorts with different ages at metabolic profiling, but the magnitudes were weak. The pattern of metabolic deviations associated with lower birthweight resembled the metabolic signature of higher adult BMI (R2 = 0.77) assessed at the same time as the metabolic profiling. The resemblance indicated that 1 kg lower birthweight is associated with similar metabolic aberrations as caused by 0.92 units higher BMI in adulthood. Conclusions: Lower birthweight adjusted for gestational age is associated with adverse biomarker aberrations across multiple metabolic pathways. Coherent metabolic signatures between lower birthweight and higher adult adiposity suggest that shared molecular pathways may potentially underpin the metabolic deviations. However, the magnitudes of metabolic associations with birthweight are modest in comparison to the effects of adiposity
Published: 2016

26. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk

Author: Ehret, G.B. (Georg), Munroe, P. (Patricia), Rice, K.M. (Kenneth), Bochud, M. (Murielle), Johnson, A.D. (Andrew), Chasman, D.I. (Daniel), Smith, A.V. (Albert Vernon), Tobin, M.D. (Martin), Verwoert, G.C. (Germaine), Hwang, S.J., Pihur, V. (Vasyl), Vollenweider, P. (Peter), O'Reilly, P.F. (Paul), Amin, N. (Najaf), Bragg-Gresham, J.L. (Jennifer L.), Teumer, A. (Alexander), Glazer, N.L. (Nicole), Launer, L.J. (Lenore), Zhao, J.H. (Jing Hua), Aulchenko, Y.S. (Yurii), Heath, S.C. (Simon), Sõber, S. (Siim), Parsa, A. (Afshin), Luan, J., Arora, P. (Pankaj), Dehghan, A. (Abbas), Zhang, F. (Feng), Lucas, G. (Gavin), Hicks, A.A. (Andrew), Jackson, A.U. (Anne), Peden, J. (John), Tanaka, T. (Toshiko), Wild, S.H. (Sarah), Rudan, I. (Igor), Igl, W. (Wilmar), Milaneschi, Y. (Yuri), Parker, A.N. (Alex), Fava, C. (Cristiano), Chambers, J.C. (John), Fox, E.R. (Ervin), Kumari, M. (Meena), Jin Go, M. (Min), Harst, P. (Pim) van der, Kao, W.-H.L. (Wen-Hong Linda), Sjögren, M. (Marketa), Vinay, D.G., Alexander, M. (Myriam), Tabara, Y. (Yasuharu), Shaw-Hawkins, S. (Sue), Whincup, P.H. (Peter), Liu, Y. (YongMei), Shi, G. (Gang), Kuusisto, J. (Johanna), Tayo, B. (Bamidele), Seielstad, M. (Mark), Sim, X. (Xueling), Hoang Nguyen, K.-D., Lehtimäki, T. (Terho), Matullo, G. (Giuseppe), Wu, Y. (Ying), Gaunt, T.R. (Tom), Onland-Moret, N.C. (Charlotte), Cooper, M.N. (Matthew), Platou, C. (Carl), Org, E. (Elin), Hardy, R. (Rebecca), Dahgam, S. (Santosh), Palmen, J. (Jutta), Vitart, V. (Veronique), Braund, P.S. (Peter), Kuznetsova, T. (Tatiana), Uiterwaal, C.S.P.M. (Cuno), Adeyemo, A. (Adebowale), Palmas, W. (Walter), Campbell, H. (Harry), Ludwig, B. (Barbara), Tomaszewski, M., Tzoulaki, I., Palmer, N.D. (Nicholette), Aspelund, T. (Thor), Garcia, M. (Melissa), Chang, Y.-P.C. (Yen-Pei), O´Connell, J.R., Steinle, N.I. (Nanette), Grobbee, D.E. (Diederick), Arking, D.E. (Dan), Kardia, S.L. (Sharon), Morrison, A.C. (Alanna), Hernandez, D.G. (Dena), Najjar, S.S. (Samer), McArdle, W.L. (Wendy), Hadley, D. (David), Brown, M.J. (Morris), Connell, J. (John), Hingorani, A. (Aroon), Day, I.N.M. (Ian), Lawlor, D.A. (Debbie), Beilby, J.P. (John), Lawrence, R.W. (Robert), Clarke, R., Hopewell, J., Ongen, H. (Halit), Dreisbach, A.W. (Albert), Li, Y. (Yali), Hunter Young, J., Bis, J.C. (Joshua), Kähönen, M. (Mika), Viikari, J. (Jorma), Lee, N.R. (Nanette), Chen, M-H. (Ming-Huei), Olden, M. (Matthias), Pattaro, C. (Cristian), Hoffman Bolton, J.A. (Judith), Köttgen, A. (Anna), Bergmann, S.M. (Sven), Mooser, V. (Vincent), Chaturvedi, N. (Nish), Frayling, T.M. (Timothy), Islam, M. (Muhammad), Jafar, T.H. (Tazeen), Kulkarni, S.R. (Smita), Bornstein, S.R. (Stefan), Gräßler, J. (Jürgen), Groop, L. (Leif), Voight, B.F. (Benjamin), Kettunen, J. (Johannes), Howard, P. (Philip), Taylor, A. (Andrew), Guarrera, S. (Simonetta), Ricceri, F. (Fulvio), Emilsson, V. (Valur), Plump, A.S. (Andrew), Khaw, K-T. (Kay-Tee), Weder, A.B. (Alan), Hunt, S.C. (Steven), Sun, Y.V. (Yan), Bergman, R.N. (Richard), Collins, F.S. (Francis), Bonnycastle, L.L. (Lori), Scott, L.J. (Laura), Stringham, H.M. (Heather), Peltonen, L. (Leena Johanna), Perola, M. (Markus), Vartiainen, E. (Erkki), Brand, S.-M., Staessen, J.A. (Jan), Wang, Y.A. (Ying), Burton, P.R. (Paul), Soler Artigas, M. (Maria), Dong, Y. (Yanbin), Snieder, H. (Harold), Zhu, H. (Haidong), Lohman, K. (Kurt), Rudock, M.E. (Megan), Heckbert, S.R. (Susan), Wiggins, K.L. (Kerri), Doumatey, A. (Ayo), Shriner, D. (Daniel), Veldre, G. (Gudrun), Viigimaa, M. (Margus), Kinra, S. (Sanjay), Prabhakaran, D. (Dorairaj), Tripathy, V. (Vikal), Langefeld, C.D. (Carl), Rosengren, A. (Annika), Thelle, D.S. (Dag), Corsi, A.M. (Anna Maria), Singleton, A. (Andrew), Forrester, T. (Terrence), Hilton, G. (Gina), McKenzie, C.A. (Colin), Salako, T. (Tunde), Iwai, N. (Naoharu), Kita, Y. (Yoshikuni), Ogihara, T. (Toshio), Ohkubo, T. (Takayoshi), Okamura, T. (Tomonori), Ueshima, H. (Hirotsugu), Umemura, S. (Satoshi), Eyheramendy, S. (Susana), Meitinger, T. (Thomas), Wichmann, H.E. (Heinz Erich), Shin Cho, Y. (Yoon), Kim, H.-L., Sehmi, J.S. (Joban), Hedblad, B. (Bo), Nilsson, P. (Peter), Davey-Smith, G. (George), Wong, A. (Andrew), Narisu, N. (Narisu), Stancáková, A. (Alena), Raffel, L.J. (Leslie), Yao, J. (Jie), Kathiresan, S. (Sekar), O'Donnell, C.J. (Christopher), Schwartz, S.M. (Stephen), Ikram, M.A. (Arfan), Longstreth Jr, W.T., Mosley, T.H. (Thomas), Seshadri, S. (Sudha), Shrine, N.R.G. (Nick), Wain, L.V. (Louise), Morken, M.A. (Mario), Swift, A.J. (Amy), Laitinen, J. (Jaana), Prokopenko, I. (Inga), Zitting, P. (Paavo), Humphries, S.E. (Steve), Danesh, J. (John), Rasheed, A. (Asif), Goel, A. (Anuj), Hamsten, A. (Anders), Watkins, H. (Hugh), Gilst, W.H. (Wiek) van, Janipalli, C.S. (Charles), Radha Mani, K., Yajnik, C. (Chittaranjan), Hofman, A. (Albert), Mattace Raso, F.U.S. (Francesco), Oostra, B.A. (Ben), Demirkan, A. (Ayşe), Isaacs, A.J. (Aaron), Rivadeneira Ramirez, F. (Fernando), Lakatta, E. (Edward), Orrù, M. (Marco), Scuteri, A. (Angelo), Ala-Korpela, M. (Mika), Kangas, A.J. (Antti), Lyytikäinen, L.-P. (Leo-Pekka), Soininen, P. (Pasi), Tukiainen, T. (Taru), Würtz, P. (Peter), Twee-Hee Ong, R. (Rick), Dörr, M. (Marcus), Kroemer, H.K. (Heyo), Völker, U. (Uwe), Völzke, H. (Henry), Galan, P. (Pilar), Hercberg, S. (Serge), Lathrop, G.M. (Mark), Zelenika, D. (Diana), Deloukas, P. (Panagiotis), Mangino, M. (Massimo), Spector, T.D. (Timothy), Zhai, G. (Guangju), Meschia, J.F. (James F.), Nalls, M.A. (Michael), Sharma, P. (Pankaj), Terzic, J. (Janos), Kranthi Kumar, M.V., Denniff, M. (Matthew), Zukowska-Szczechowska, E. (Ewa), Wagenknecht, L.E. (Lynne), Fowkes, F.G.R. (Gerald), Charchar, F.J. (Fadi), Schwarz, P.E.H. (Peter), Hayward, C. (Caroline), Guo, X. (Xiuqing), Rotimi, C. (Charles), Bots, M.L. (Michiel), Samani, N.J. (Nilesh), Polasek, O. (Ozren), Talmud, P.J. (Philippa), Nyberg, F. (Fredrik), Kuh, D. (Diana), Laan, M. (Maris), Hveem, K. (Kristian), Schouw, Y.T. (Yvonne) van der, Casas, J.P. (Juan), Mohlke, K.L. (Karen), Vineis, P. (Paolo), Raitakari, O. (Olli), Ganesh, S.K. (Santhi), Shyong Tai, E., Laakso, M. (Markku), Rao, D.C. (Dabeeru C.), Harris, T.B. (Tamara), Morris, R.W. (Richard), Dominiczak, A. (Anna), Kivimaki, M. (Mika), Marmot, M. (Michael), Miki, T. (Tetsuro), Saleheen, D., Chandak, G.R. (Giriraj), Coresh, J. (Josef), Navis, G. (Gerjan), Salomaa, V. (Veikko), Han, B.-G., Kooner, J.S. (Jaspal), Melander, O. (Olle), Ridker, P.M. (Paul), Bandinelli, S. (Stefania), Gyllensten, U. (Ulf), Wright, A.F. (Alan), Wilson, J.F. (James), Ferrucci, L. (Luigi), Farrall, M. (Martin), Tuomilehto, J. (Jaakko), Pramstaller, P.P. (Peter Paul), Elosua, R. (Roberto), Soranzo, N. (Nicole), Sijbrands, E.J.G. (Eric), Altshuler, D. (David), Loos, R.J.F. (Ruth), Shuldiner, A.R. (Alan), Gieger, C. (Christian), Meneton, P. (Pierre), Uitterlinden, A.G. (André), Wareham, N.J. (Nick), Gudnason, V. (Vilmundur), Rotter, J.I. (Jerome), Rettig, R. (Rainer), Uda, M. (Manuela), Strachan, D.P. (David), Witteman, J.C.M. (Jacqueline), Hartikainen, A.L., Beckmann, J.S. (Jacques), Boerwinkle, E.A. (Eric), Erdmann, J. (Jeanette), Vasan, R.S. (Ramachandran Srini), Boehnke, M. (Michael), Larson, M.G. (Martin), Järvelin, M.R., Psaty, B.M. (Bruce), Tikka-Kleemola, P. (Päivi), Newton-Cheh, C. (Christopher), Elliott, P. (Paul), Levy, D. (Daniel), Caulfield, M. (Mark), Abecasis, G.R. (Gonçalo), Adair, L.S. (Linda), Bakker, S.J.L. (Stephan), Barroso, I.E. (Inês), Ehret, G.B. (Georg), Munroe, P. (Patricia), Rice, K.M. (Kenneth), Bochud, M. (Murielle), Johnson, A.D. (Andrew), Chasman, D.I. (Daniel), Smith, A.V. (Albert Vernon), Tobin, M.D. (Martin), Verwoert, G.C. (Germaine), Hwang, S.J., Pihur, V. (Vasyl), Vollenweider, P. (Peter), O'Reilly, P.F. (Paul), Amin, N. (Najaf), Bragg-Gresham, J.L. (Jennifer L.), Teumer, A. (Alexander), Glazer, N.L. (Nicole), Launer, L.J. (Lenore), Zhao, J.H. (Jing Hua), Aulchenko, Y.S. (Yurii), Heath, S.C. (Simon), Sõber, S. (Siim), Parsa, A. (Afshin), Luan, J., Arora, P. (Pankaj), Dehghan, A. (Abbas), Zhang, F. (Feng), Lucas, G. (Gavin), Hicks, A.A. (Andrew), Jackson, A.U. (Anne), Peden, J. (John), Tanaka, T. (Toshiko), Wild, S.H. (Sarah), Rudan, I. (Igor), Igl, W. (Wilmar), Milaneschi, Y. (Yuri), Parker, A.N. (Alex), Fava, C. (Cristiano), Chambers, J.C. (John), Fox, E.R. (Ervin), Kumari, M. (Meena), Jin Go, M. (Min), Harst, P. (Pim) van der, Kao, W.-H.L. (Wen-Hong Linda), Sjögren, M. (Marketa), Vinay, D.G., Alexander, M. (Myriam), Tabara, Y. (Yasuharu), Shaw-Hawkins, S. (Sue), Whincup, P.H. (Peter), Liu, Y. (YongMei), Shi, G. (Gang), Kuusisto, J. (Johanna), Tayo, B. (Bamidele), Seielstad, M. (Mark), Sim, X. (Xueling), Hoang Nguyen, K.-D., Lehtimäki, T. (Terho), Matullo, G. (Giuseppe), Wu, Y. (Ying), Gaunt, T.R. (Tom), Onland-Moret, N.C. (Charlotte), Cooper, M.N. (Matthew), Platou, C. (Carl), Org, E. (Elin), Hardy, R. (Rebecca), Dahgam, S. (Santosh), Palmen, J. (Jutta), Vitart, V. (Veronique), Braund, P.S. (Peter), Kuznetsova, T. (Tatiana), Uiterwaal, C.S.P.M. (Cuno), Adeyemo, A. (Adebowale), Palmas, W. (Walter), Campbell, H. (Harry), Ludwig, B. (Barbara), Tomaszewski, M., Tzoulaki, I., Palmer, N.D. (Nicholette), Aspelund, T. (Thor), Garcia, M. (Melissa), Chang, Y.-P.C. (Yen-Pei), O´Connell, J.R., Steinle, N.I. (Nanette), Grobbee, D.E. (Diederick), Arking, D.E. (Dan), Kardia, S.L. (Sharon), Morrison, A.C. (Alanna), Hernandez, D.G. (Dena), Najjar, S.S. (Samer), McArdle, W.L. (Wendy), Hadley, D. (David), Brown, M.J. (Morris), Connell, J. (John), Hingorani, A. (Aroon), Day, I.N.M. (Ian), Lawlor, D.A. (Debbie), Beilby, J.P. (John), Lawrence, R.W. (Robert), Clarke, R., Hopewell, J., Ongen, H. (Halit), Dreisbach, A.W. (Albert), Li, Y. (Yali), Hunter Young, J., Bis, J.C. (Joshua), Kähönen, M. (Mika), Viikari, J. (Jorma), Lee, N.R. (Nanette), Chen, M-H. (Ming-Huei), Olden, M. (Matthias), Pattaro, C. (Cristian), Hoffman Bolton, J.A. (Judith), Köttgen, A. (Anna), Bergmann, S.M. (Sven), Mooser, V. (Vincent), Chaturvedi, N. (Nish), Frayling, T.M. (Timothy), Islam, M. (Muhammad), Jafar, T.H. (Tazeen), Kulkarni, S.R. (Smita), Bornstein, S.R. (Stefan), Gräßler, J. (Jürgen), Groop, L. (Leif), Voight, B.F. (Benjamin), Kettunen, J. (Johannes), Howard, P. (Philip), Taylor, A. (Andrew), Guarrera, S. (Simonetta), Ricceri, F. (Fulvio), Emilsson, V. (Valur), Plump, A.S. (Andrew), Khaw, K-T. (Kay-Tee), Weder, A.B. (Alan), Hunt, S.C. (Steven), Sun, Y.V. (Yan), Bergman, R.N. (Richard), Collins, F.S. (Francis), Bonnycastle, L.L. (Lori), Scott, L.J. (Laura), Stringham, H.M. (Heather), Peltonen, L. (Leena Johanna), Perola, M. (Markus), Vartiainen, E. (Erkki), Brand, S.-M., Staessen, J.A. (Jan), Wang, Y.A. (Ying), Burton, P.R. (Paul), Soler Artigas, M. (Maria), Dong, Y. (Yanbin), Snieder, H. (Harold), Zhu, H. (Haidong), Lohman, K. (Kurt), Rudock, M.E. (Megan), Heckbert, S.R. (Susan), Wiggins, K.L. (Kerri), Doumatey, A. (Ayo), Shriner, D. (Daniel), Veldre, G. (Gudrun), Viigimaa, M. (Margus), Kinra, S. (Sanjay), Prabhakaran, D. (Dorairaj), Tripathy, V. (Vikal), Langefeld, C.D. (Carl), Rosengren, A. (Annika), Thelle, D.S. (Dag), Corsi, A.M. (Anna Maria), Singleton, A. (Andrew), Forrester, T. (Terrence), Hilton, G. (Gina), McKenzie, C.A. (Colin), Salako, T. (Tunde), Iwai, N. (Naoharu), Kita, Y. (Yoshikuni), Ogihara, T. (Toshio), Ohkubo, T. (Takayoshi), Okamura, T. (Tomonori), Ueshima, H. (Hirotsugu), Umemura, S. (Satoshi), Eyheramendy, S. (Susana), Meitinger, T. (Thomas), Wichmann, H.E. (Heinz Erich), Shin Cho, Y. (Yoon), Kim, H.-L., Sehmi, J.S. (Joban), Hedblad, B. (Bo), Nilsson, P. (Peter), Davey-Smith, G. (George), Wong, A. (Andrew), Narisu, N. (Narisu), Stancáková, A. (Alena), Raffel, L.J. (Leslie), Yao, J. (Jie), Kathiresan, S. (Sekar), O'Donnell, C.J. (Christopher), Schwartz, S.M. (Stephen), Ikram, M.A. (Arfan), Longstreth Jr, W.T., Mosley, T.H. (Thomas), Seshadri, S. (Sudha), Shrine, N.R.G. (Nick), Wain, L.V. (Louise), Morken, M.A. (Mario), Swift, A.J. (Amy), Laitinen, J. (Jaana), Prokopenko, I. (Inga), Zitting, P. (Paavo), Humphries, S.E. (Steve), Danesh, J. (John), Rasheed, A. (Asif), Goel, A. (Anuj), Hamsten, A. (Anders), Watkins, H. (Hugh), Gilst, W.H. (Wiek) van, Janipalli, C.S. (Charles), Radha Mani, K., Yajnik, C. (Chittaranjan), Hofman, A. (Albert), Mattace Raso, F.U.S. (Francesco), Oostra, B.A. (Ben), Demirkan, A. (Ayşe), Isaacs, A.J. (Aaron), Rivadeneira Ramirez, F. (Fernando), Lakatta, E. (Edward), Orrù, M. (Marco), Scuteri, A. (Angelo), Ala-Korpela, M. (Mika), Kangas, A.J. (Antti), Lyytikäinen, L.-P. (Leo-Pekka), Soininen, P. (Pasi), Tukiainen, T. (Taru), Würtz, P. (Peter), Twee-Hee Ong, R. (Rick), Dörr, M. (Marcus), Kroemer, H.K. (Heyo), Völker, U. (Uwe), Völzke, H. (Henry), Galan, P. (Pilar), Hercberg, S. (Serge), Lathrop, G.M. (Mark), Zelenika, D. (Diana), Deloukas, P. (Panagiotis), Mangino, M. (Massimo), Spector, T.D. (Timothy), Zhai, G. (Guangju), Meschia, J.F. (James F.), Nalls, M.A. (Michael), Sharma, P. (Pankaj), Terzic, J. (Janos), Kranthi Kumar, M.V., Denniff, M. (Matthew), Zukowska-Szczechowska, E. (Ewa), Wagenknecht, L.E. (Lynne), Fowkes, F.G.R. (Gerald), Charchar, F.J. (Fadi), Schwarz, P.E.H. (Peter), Hayward, C. (Caroline), Guo, X. (Xiuqing), Rotimi, C. (Charles), Bots, M.L. (Michiel), Samani, N.J. (Nilesh), Polasek, O. (Ozren), Talmud, P.J. (Philippa), Nyberg, F. (Fredrik), Kuh, D. (Diana), Laan, M. (Maris), Hveem, K. (Kristian), Schouw, Y.T. (Yvonne) van der, Casas, J.P. (Juan), Mohlke, K.L. (Karen), Vineis, P. (Paolo), Raitakari, O. (Olli), Ganesh, S.K. (Santhi), Shyong Tai, E., Laakso, M. (Markku), Rao, D.C. (Dabeeru C.), Harris, T.B. (Tamara), Morris, R.W. (Richard), Dominiczak, A. (Anna), Kivimaki, M. (Mika), Marmot, M. (Michael), Miki, T. (Tetsuro), Saleheen, D., Chandak, G.R. (Giriraj), Coresh, J. (Josef), Navis, G. (Gerjan), Salomaa, V. (Veikko), Han, B.-G., Kooner, J.S. (Jaspal), Melander, O. (Olle), Ridker, P.M. (Paul), Bandinelli, S. (Stefania), Gyllensten, U. (Ulf), Wright, A.F. (Alan), Wilson, J.F. (James), Ferrucci, L. (Luigi), Farrall, M. (Martin), Tuomilehto, J. (Jaakko), Pramstaller, P.P. (Peter Paul), Elosua, R. (Roberto), Soranzo, N. (Nicole), Sijbrands, E.J.G. (Eric), Altshuler, D. (David), Loos, R.J.F. (Ruth), Shuldiner, A.R. (Alan), Gieger, C. (Christian), Meneton, P. (Pierre), Uitterlinden, A.G. (André), Wareham, N.J. (Nick), Gudnason, V. (Vilmundur), Rotter, J.I. (Jerome), Rettig, R. (Rainer), Uda, M. (Manuela), Strachan, D.P. (David), Witteman, J.C.M. (Jacqueline), Hartikainen, A.L., Beckmann, J.S. (Jacques), Boerwinkle, E.A. (Eric), Erdmann, J. (Jeanette), Vasan, R.S. (Ramachandran Srini), Boehnke, M. (Michael), Larson, M.G. (Martin), Järvelin, M.R., Psaty, B.M. (Bruce), Tikka-Kleemola, P. (Päivi), Newton-Cheh, C. (Christopher), Elliott, P. (Paul), Levy, D. (Daniel), Caulfield, M. (Mark), Abecasis, G.R. (Gonçalo), Adair, L.S. (Linda), Bakker, S.J.L. (Stephan), and Barroso, I.E. (Inês)
Abstract: Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140mmg Hg systolic blood pressure ≥90mmg Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3 GUCY1B3, NPR3 C5orf23, ADM, FURIN FES, GOSR2, GNAS EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 geno
Published: 2011
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27. A genome-wide screen for interactions reveals a new locus on 4p15 modifying the effect of waist-to-hip ratio on total cholesterol

Author: Surakka, I. (Ida), Isaacs, A.J. (Aaron), Karssen, L.C. (Lennart), Laurila, P.-P.P., Middelberg, R.P.S. (Rita), Tikkanen, E. (Emmi), Ried, J.S. (Janina), Lamina, C. (Claudia), Mangino, M. (Massimo), Igl, W. (Wilmar), Hottenga, J.J. (Jouke Jan), Lagou, V. (Vasiliki), Harst, P. (Pim) van der, Leach, I.M. (Irene Mateo), Esko, T. (Tõnu), Kutalik, Z. (Zoltán), Wainwright, N.W. (Nicholas), Struchalin, M.V. (Maksim), Sarin, A.-P., Kangas, A.J. (Antti), Viikari, J. (Jorma), Perola, M. (Markus), Rantanen, T. (Taina), Petersen, A.K., Soininen, P. (Pasi), Johansson, A. (Åsa), Soranzo, N. (Nicole), Heath, A.C. (Andrew), Papamarkou, T. (Theodore), Prokopenko, I. (Inga), Tönjes, A. (Anke), Kronenberg, F. (Florian), Döring, A. (Angela), Rivadeneira Ramirez, F. (Fernando), Montgomery, G.W. (Grant), Whitfield, J.B. (John), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Freimer, N.B. (Nelson), Willemsen, G.A.H.M. (Gonneke), Geus, E.J.C. (Eco) de, Palotie, A. (Aarno), Sandhu, M.S. (Manj), Waterworth, D. (Dawn), Metspalu, A. (Andres), Stumvoll, M. (Michael), Uitterlinden, A.G. (André), Jula, A. (Antti), Navis, G. (Gerjan), Wijmenga, C. (Cisca), Wolffenbuttel, B.H.R. (Bruce), Taskinen, M.-R., Ala-Korpela, M. (Mika), Kaprio, J. (Jaakko), Kyvik, K.O. (Kirsten Ohm), Boomsma, D.I. (Dorret), Pedersen, N.L. (Nancy), Gyllensten, U. (Ulf), Wilson, J.F. (James), Rudan, I. (Igor), Campbell, H. (Harry), Pramstaller, P.P. (Peter Paul), Spector, T.D. (Timothy), Witteman, J.C.M. (Jacqueline), Eriksson, J.G. (Johan), Salomaa, V. (Veikko), Oostra, B.A. (Ben), Raitakari, O. (Olli), Wichmann, H.E. (Heinz Erich), Gieger, C. (Christian), Järvelin, M.R., Martin, N.G. (Nicholas), Hofman, A. (Albert), McCarthy, M.I. (Mark), Aulchenko, Y.S. (Yurii), Peltonen, L. (Leena Johanna), Tikka-Kleemola, P. (Päivi), Ripatti, S. (Samuli), Surakka, I. (Ida), Isaacs, A.J. (Aaron), Karssen, L.C. (Lennart), Laurila, P.-P.P., Middelberg, R.P.S. (Rita), Tikkanen, E. (Emmi), Ried, J.S. (Janina), Lamina, C. (Claudia), Mangino, M. (Massimo), Igl, W. (Wilmar), Hottenga, J.J. (Jouke Jan), Lagou, V. (Vasiliki), Harst, P. (Pim) van der, Leach, I.M. (Irene Mateo), Esko, T. (Tõnu), Kutalik, Z. (Zoltán), Wainwright, N.W. (Nicholas), Struchalin, M.V. (Maksim), Sarin, A.-P., Kangas, A.J. (Antti), Viikari, J. (Jorma), Perola, M. (Markus), Rantanen, T. (Taina), Petersen, A.K., Soininen, P. (Pasi), Johansson, A. (Åsa), Soranzo, N. (Nicole), Heath, A.C. (Andrew), Papamarkou, T. (Theodore), Prokopenko, I. (Inga), Tönjes, A. (Anke), Kronenberg, F. (Florian), Döring, A. (Angela), Rivadeneira Ramirez, F. (Fernando), Montgomery, G.W. (Grant), Whitfield, J.B. (John), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Freimer, N.B. (Nelson), Willemsen, G.A.H.M. (Gonneke), Geus, E.J.C. (Eco) de, Palotie, A. (Aarno), Sandhu, M.S. (Manj), Waterworth, D. (Dawn), Metspalu, A. (Andres), Stumvoll, M. (Michael), Uitterlinden, A.G. (André), Jula, A. (Antti), Navis, G. (Gerjan), Wijmenga, C. (Cisca), Wolffenbuttel, B.H.R. (Bruce), Taskinen, M.-R., Ala-Korpela, M. (Mika), Kaprio, J. (Jaakko), Kyvik, K.O. (Kirsten Ohm), Boomsma, D.I. (Dorret), Pedersen, N.L. (Nancy), Gyllensten, U. (Ulf), Wilson, J.F. (James), Rudan, I. (Igor), Campbell, H. (Harry), Pramstaller, P.P. (Peter Paul), Spector, T.D. (Timothy), Witteman, J.C.M. (Jacqueline), Eriksson, J.G. (Johan), Salomaa, V. (Veikko), Oostra, B.A. (Ben), Raitakari, O. (Olli), Wichmann, H.E. (Heinz Erich), Gieger, C. (Christian), Järvelin, M.R., Martin, N.G. (Nicholas), Hofman, A. (Albert), McCarthy, M.I. (Mark), Aulchenko, Y.S. (Yurii), Peltonen, L. (Leena Johanna), Tikka-Kleemola, P. (Päivi), and Ripatti, S. (Samuli)
Abstract: Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ~25% of the heritability of the phenotypes. To date, no unbiased screen for gene-environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry (maximum N = 32,225). We collected 8 further cohorts (N = 17,102) for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10-9. There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.
Published: 2011
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28. Epidemiological applications of quantitative serum NMR metabolomics:causal inference from observational studies

Author: Wang, Q. (Qin), Kettunen, J. (Johannes), Würtz, P. (Peter), and Soininen, P. (Pasi)
Subjects: ehkäisypillerit, oral contraception, ydinmagneettinen resonanssi, Mendelian randomization analysis, poikittaistutkimus, biomarkers, Mendeliaaninen satunnaistaminen, body mass index, longitudinal analysis, biomarkkerit, pitkittäistutkimus, metabolomics, cross-sectional analysis, statins, nuclear magnetic resonance, sukupuolihormoneja sitova globuliini, sex hormone-binding globulin, painoindeksi, metabolomiikka, statiinit, metabolites, metaboliitit
Abstract: Cardiovascular diseases are the leading cause of death worldwide and type 2 diabetes is reaching a global epidemic. Epidemiological studies have identified numerous risk factors and pharmacotherapies in relation to these cardiometabolic diseases. However, the detailed molecular mechanisms of these risk factors and drug therapies generally remain incompletely understood. Elucidating the underlying molecular effects would be essential for better understanding of the disease pathogenesis and also for discovering new therapeutic targets. Quantitative serum metabolomics, which allows for simultaneous quantification of multiple circulating metabolic measures, provides a hypothesis-free approach to systematically inspect the metabolic changes in response to endogenous and exogenous stimuli. Metabolomics thus presents a valuable tool to study the detailed molecular effects of disease risk factors and drug therapies. However, current metabolomics studies are mostly conducted in small cross-sectional studies and the causal relations of the risk factors on the metabolic measures are generally unclear, providing limited public health impact. The present thesis serves as a proof-of-concept to illustrate that well-designed observational studies can be used to infer causality. With the exemplars of assessing molecular effects of two risk factors (body mass index and sex hormone-binding globulin) and two drug therapies (statins and oral contraceptives), the thesis demonstrates that an improved causal inference can be achieved in observational studies via the combination of multiple study designs, including cross-sectional, longitudinal and Mendelian randomization analysis. This robust study design approach together with metabolomics data can be also extended to study the molecular effects of other risk factors and drug therapies. With an improved molecular understanding of a wide range of risk factors and therapies, better understanding of disease pathogenesis is ensured. Tiivistelmä Sydän- ja verisuonitaudit ovat johtava kuolinsyy maailmassa ja tyypin 2 diabetes on saavuttamassa globaalin epidemian mittasuhteet. Epidemiologiset tutkimukset ovat löytäneet useita riskitekijöitä ja lääkehoitoja edellä mainituille yleisille taudeille. Tyypin 2 diabetekseen ja sydän- ja verisuonitauteihin liittyvät yksityiskohtaiset molekylaariset mekanismit ymmärretään kuitenkin puutteellisesti. Molekylaaristen yksityiskohtien tarkempi ymmärtäminen olisi siten erittäin merkittävää sekä tautiprosessien ymmärtämiseksi että lääkehoitojen kehittämiseksi. Seerumin kvantitatiivinen metabolomiikka mahdollistaa useiden metabolisten suureiden samanaikaisen määrittämisen verenkierrosta ja tarjoaa siten hypoteesittoman lähestymistavan sekä sisäisten että ulkoisten ärsykkeiden aiheuttamien metabolisten muutosten systemaattiseen tutkimukseen. Metabolomiikka on siten arvokas työkalu yksityiskohtaisten molekylaaristen mekanismien tutkimuksessa, olipa kyseessä taudin riskitekijät tai lääkehoito. Metabolomiikkatutkimuksia on kuitenkin pääasiassa tehty pienissä poikittaistutkimuksissa ja riskitekijöihin liittyvien metabolisten suureiden syy- ja seuraussuhteet ovat yleisesti epäselviä, josta johtuen metabolisten suureiden kansanterveydellinen sovellettavuus on ollut heikkoa. Tämä väitöskirja esittelee tutkimuskonseptin hyvin suunniteltujen havaintotutkimuksien soveltamiseksi syy- ja seuraussuhteiden arvioinnissa. Työ sisältää esimerkit kahden riskitekijän (painoindeksi ja sukupuolihormoneja sitova globuliini) ja kahden lääkehoidon (statiinit ja ehkäisypillerit) molekylaaristen vaikutusten kausaalisista tutkimuksista. Tulokset havainnollistavat, että kausaalisten johtopäätösten luotettavuutta voidaan parantaa yhdistämällä useita tutkimusasetelmia, kuten poikittais- ja pitkittäistutkimuksia sekä Mendelististä satunnaistamista. Esitettyjä luotettavia tutkimusasetelmia, yhdessä metabolomiikkadatan kanssa, voidaan laajentaa muiden riskitekijöiden ja lääkehoitojen molekylaaristen vaikutusten tutkimuksiin. Parantunut molekyylitason ymmärrys useista riskitekijöistä ja lääkehoidoista johtaa myös parempaan tautiprosessien ymmärtämiseen.
Published: 2017

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28 results on '"Soininen, P. (Pasi)"'

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3. Total liver phosphatidylcholine content associates with non-alcoholic steatohepatitis and glycine N-methyltransferase expression

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5. Nuclear magnetic resonance‐based metabolomics identifies phenylalanine as a novel predictor of incident heart failure hospitalisation:results from PROSPER and FINRISK 1997

6. Association of circulating metabolites with healthy diet and risk of cardiovascular disease:analysis of two cohort studies

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