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1. Comprehensive biomarker profiling of hypertension in 36 985 Finnish individuals

Author

Palmu, J. (Joonatan), Tikkanen, E. (Emmi), Havulinna, A. S. (Aki S.), Vartiainen, E. (Erkki), Lundqvist, A. (Annamari), Ruuskanen, M. O. (Matti O.), Perola, M. (Markus), Ala-Korpela, M. (Mika), Jousilahti, P. (Pekka), Würtz, P. (Peter), Salomaa, V. (Veikko), Lahti, L. (Leo), Niiranen, T. (Teemu), Palmu, J. (Joonatan), Tikkanen, E. (Emmi), Havulinna, A. S. (Aki S.), Vartiainen, E. (Erkki), Lundqvist, A. (Annamari), Ruuskanen, M. O. (Matti O.), Perola, M. (Markus), Ala-Korpela, M. (Mika), Jousilahti, P. (Pekka), Würtz, P. (Peter), Salomaa, V. (Veikko), Lahti, L. (Leo), and Niiranen, T. (Teemu)

Abstract

Objective: Previous studies on the association between metabolic biomarkers and hypertension have been limited by small sample sizes, low number of studied biomarkers, and cross-sectional study design. In the largest study to date, we assess the cross-sectional and longitudinal associations between high-abundance serum biomarkers and blood pressure (BP). Methods: We studied cross-sectional (N = 36 985; age 50.5 ± 14.2; 53.1% women) and longitudinal (N = 4197; age 49.4 ± 11.8, 55.3% women) population samples of Finnish individuals. We included 53 serum biomarkers and other detailed lipoprotein subclass measures in our analyses. We studied the associations between serum biomarkers and BP using both conventional statistical methods and a machine learning algorithm (gradient boosting) while adjusting for clinical risk factors. Results: Fifty-one of 53 serum biomarkers were cross-sectionally related to BP (adjusted P < 0.05 for all). Conventional linear regression modeling demonstrated that LDL cholesterol, remnant cholesterol, apolipoprotein B, and acetate were positively, and HDL particle size was negatively, associated with SBP change over time (adjusted P < 0.05 for all). Adding serum biomarkers (cross-sectional root-mean-square error: 16.27 mmHg; longitudinal: 17.61 mmHg) in the model with clinical measures (cross-sectional: 16.70 mmHg; longitudinal 18.52 mmHg) improved the machine learning model fit. Glucose, albumin, triglycerides in LDL, glycerol, VLDL particle size, and acetoacetate had the highest importance scores in models related to current or future BP. Conclusions: Our results suggest that serum lipids, and particularly LDL-derived and VLDL-derived cholesterol measures, and glucose metabolism abnormalities are associated with hypertension onset. Use of serum metabolite determination could improve identification of individuals at high risk of developing hypertension.

Published
2022

2. Metabolic biomarker discovery for risk of peripheral artery disease compared with coronary artery disease:lipoprotein and metabolite profiling of 31 657 individuals from 5 prospective cohorts

Author

Tikkanen, E. (Emmi), Jägerroos, V. (Vilma), Holmes, M. V. (Michael V.), Sattar, N. (Naveed), Ala‐Korpela, M. (Mika), Jousilahti, P. (Pekka), Lundqvist, A. (Annamari), Perola, M. (Markus), Salomaa, V. (Veikko), and Würtz, P. (Peter)

Subjects
biomarker, peripheral artery disease, magnetic resonance spectroscopy, metabolomics, coronary artery disease
Abstract

Background: Peripheral artery disease (PAD) and coronary artery disease (CAD) represent atherosclerosis in different vascular beds. We used detailed metabolic biomarker profiling to identify common and discordant biomarkers and clarify pathophysiological differences for these vascular diseases. Methods and results: We used 5 prospective cohorts from Finnish population (FINRISK 1997, 2002, 2007, and 2012, and Health 2000; n=31 657; median follow‐up time of 14 years) to estimate associations between >200 metabolic biomarkers and incident PAD and CAD. Metabolic biomarkers were measured with nuclear magnetic resonance, and disease events were obtained from nationwide hospital records. During the follow‐up, 498 incident PAD and 2073 incident CAD events occurred. In age‐ and sex‐adjusted Cox models, apolipoproteins and cholesterol measures were robustly associated with incident CAD (eg, hazard ratio [HR] per SD for higher apolipoprotein B/A‐1 ratio, 1.30; 95% CI, 1.25–1.36), but not with incident PAD (HR per SD for higher apolipoprotein B/A‐1 ratio, 1.04; 95% CI, 0.95–1.14; Pheterogeneity

Published
2021

3. Investigating the effects of lycopene and green tea on the metabolome of men at risk of prostate cancer:the ProDiet randomised controlled trial

Author

Beynon, R. A. (Rhona A.), Richmond, R. C. (Rebecca C.), Santos Ferreira, D. L. (Diana L.), Ness, A. R. (Andrew R.), May, M. (Margaret), Smith, G. D. (George Davey), Vincent, E. E. (Emma E.), Adams, C. (Charleen), Ala‐Korpela, M. (Mika), Würtz, P. (Peter), Soidinsalo, S. (Sebastian), Metcalfe, C. (Christopher), Donovan, J. L. (Jenny L.), Lane, A. J. (Athene J.), Martin, R. M. (Richard M.), P. S. (ProtecT Study Group), and P. c. (PRACTICAL consortium)

Subjects
dietary intervention, green tea, lycopene, prostate cancer, mendelian randomisation
Abstract

Lycopene and green tea consumption have been observationally associated with reduced prostate cancer risk, but the underlying mechanisms have not been fully elucidated. We investigated the effect of factorial randomisation to a 6‐month lycopene and green tea dietary advice or supplementation intervention on 159 serum metabolite measures in 128 men with raised PSA levels (but prostate cancer‐free), analysed by intention‐to‐treat. The causal effects of metabolites modified by the intervention on prostate cancer risk were then assessed by Mendelian randomisation, using summary statistics from 44,825 prostate cancer cases and 27,904 controls. The systemic effects of lycopene and green tea supplementation on serum metabolic profile were comparable to the effects of the respective dietary advice interventions (R² = 0.65 and 0.76 for lycopene and green tea respectively). Metabolites which were altered in response to lycopene supplementation were acetate [β (standard deviation difference vs. placebo): 0.69; 95% CI = 0.24, 1.15; p = 0.003], valine (β: −0.62; −1.03, −0.02; p = 0.004), pyruvate (β: −0.56; −0.95, −0.16; p = 0.006) and docosahexaenoic acid (β: −0.50; −085, −0.14; p = 0.006). Valine and diacylglycerol were lower in the lycopene dietary advice group (β: −0.65; −1.04, −0.26; p = 0.001 and β: −0.59; −1.01, −0.18; p = 0.006). A genetically instrumented SD increase in pyruvate increased the odds of prostate cancer by 1.29 (1.03, 1.62; p = 0.027). An intervention to increase lycopene intake altered the serum metabolome of men at risk of prostate cancer. Lycopene lowered levels of pyruvate, which our Mendelian randomisation analysis suggests may be causally related to reduced prostate cancer risk.

Published
2019

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

Author

Deelen, J. (Joris), Kettunen, J. (Johannes), Fischer, K. (Krista), Spek, A. (Ashley) van der, Trompet, S. (Stella), Kastenmuller, G. (Gabi), Boyd, A. (Andy), Zierer, J. (Jonas), Akker, E.B. (Erik) van den, Ala-Korpela, M. (Mika), Amin, N. (Najaf), Demirkan, A. (Ayse), Ghanbari, M. (Mohsen), Heemst, D. (Diana) van, Ikram, M.A. (Arfan), Klinken, J.B. (Jan Bert) van, Mooijaart, S.P. (Simon), Peters, A. (Annette), Salomaa, V. (Veikko), Sattar, N. (Naveed), Spector, T.D. (Timothy), Tiemeier, H.W. (Henning), Verhoeven, A. (Aswin), Waldenberger, M. (Melanie), Würtz, P. (Peter), Davey Smith, G. (George), Metspalu, A. (Andres), Perola, M. (Markus), Menni, C. (Cristina), Geleijnse, J.M. (Johanna M.), Drenos, F. (Fotios), Beekman, M. (Marian), Jukema, J.W. (Jan Wouter), Duijn, C.M. (Cornelia) van, Slagboom, P.E. (Eline), Deelen, J. (Joris), Kettunen, J. (Johannes), Fischer, K. (Krista), Spek, A. (Ashley) van der, Trompet, S. (Stella), Kastenmuller, G. (Gabi), Boyd, A. (Andy), Zierer, J. (Jonas), Akker, E.B. (Erik) van den, Ala-Korpela, M. (Mika), Amin, N. (Najaf), Demirkan, A. (Ayse), Ghanbari, M. (Mohsen), Heemst, D. (Diana) van, Ikram, M.A. (Arfan), Klinken, J.B. (Jan Bert) van, Mooijaart, S.P. (Simon), Peters, A. (Annette), Salomaa, V. (Veikko), Sattar, N. (Naveed), Spector, T.D. (Timothy), Tiemeier, H.W. (Henning), Verhoeven, A. (Aswin), Waldenberger, M. (Melanie), Würtz, P. (Peter), Davey Smith, G. (George), Metspalu, A. (Andres), Perola, M. (Markus), Menni, C. (Cristina), Geleijnse, J.M. (Johanna M.), Drenos, F. (Fotios), Beekman, M. (Marian), Jukema, J.W. (Jan Wouter), Duijn, C.M. (Cornelia) van, and Slagboom, P.E. (Eline)

Abstract

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

Published
2019
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5. Elevated serum alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality

Author

Ritchie, S. C. (Scott C.), Kettunen, J. (Johannes), Brozynska, M. (Marta), Nath, A. P. (Artika P.), Havulinna, A. S. (Aki S.), Männistö, S. (Satu), Perola, M. (Markus), Salomaa, V. (Veikko), Ala-Korpela, M. (Mika), Abraham, G. (Gad), Würtz, P. (Peter), Inouye, M. (Michael), Ritchie, S. C. (Scott C.), Kettunen, J. (Johannes), Brozynska, M. (Marta), Nath, A. P. (Artika P.), Havulinna, A. S. (Aki S.), Männistö, S. (Satu), Perola, M. (Markus), Salomaa, V. (Veikko), Ala-Korpela, M. (Mika), Abraham, G. (Gad), Würtz, P. (Peter), and Inouye, M. (Michael)

Abstract

Background: GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown. Methods: We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT. Results: Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10−10), influenza and pneumonia (HR = 1.37, P = 6×10−10), and liver diseases (HR = 1.81, P = 1×10−6). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways. Conclusions: This study clarifies the molecular underpinnings of the GlycA biomarker’s associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.

Published
2019

6. Circulating metabolites and the risk of type 2 diabetes:a prospective study of 11,896 young adults from four Finnish cohorts

Author

Ahola-Olli, A. V. (Ari V.), Mustelin, L. (Linda), Kalimeri, M. (Maria), Kettunen, J. (Johannes), Jokelainen, J. (Jari), Auvinen, J. (Juha), Puukka, K. (Katri), Havulinna, A. S. (Aki S.), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Juonala, M. (Markus), Keinänen-Kiukaanniem, S. (Sirkka), Salomaa, V. (Veikko), Perola, M. (Markus), Järvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), Raitakari, O. (Olli), Würtz, P. (Peter), Ahola-Olli, A. V. (Ari V.), Mustelin, L. (Linda), Kalimeri, M. (Maria), Kettunen, J. (Johannes), Jokelainen, J. (Jari), Auvinen, J. (Juha), Puukka, K. (Katri), Havulinna, A. S. (Aki S.), Lehtimäki, T. (Terho), Kähönen, M. (Mika), Juonala, M. (Markus), Keinänen-Kiukaanniem, S. (Sirkka), Salomaa, V. (Veikko), Perola, M. (Markus), Järvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), Raitakari, O. (Olli), and Würtz, P. (Peter)

Abstract

Aims/hypothesis: Metabolomics technologies have identified numerous blood biomarkers for type 2 diabetes risk in case−control studies of middle-aged and older individuals. We aimed to validate existing and identify novel metabolic biomarkers predictive of future diabetes in large cohorts of young adults. Methods: NMR metabolomics was used to quantify 229 circulating metabolic measures in 11,896 individuals from four Finnish observational cohorts (baseline age 24–45 years). Associations between baseline metabolites and risk of developing diabetes during 8–15 years of follow-up (392 incident cases) were adjusted for sex, age, BMI and fasting glucose. Prospective metabolite associations were also tested with fasting glucose, 2 h glucose and HOMA-IR at follow-up. Results: Out of 229 metabolic measures, 113 were associated with incident type 2 diabetes in meta-analysis of the four cohorts (ORs per 1 SD: 0.59–1.50; p< 0.0009). Among the strongest biomarkers of diabetes risk were branched-chain and aromatic amino acids (OR 1.31–1.33) and triacylglycerol within VLDL particles (OR 1.33–1.50), as well as linoleic n-6 fatty acid (OR 0.75) and non-esterified cholesterol in large HDL particles (OR 0.59). The metabolic biomarkers were more strongly associated with deterioration in post-load glucose and insulin resistance than with future fasting hyperglycaemia. A multi-metabolite score comprised of phenylalanine, non-esterified cholesterol in large HDL and the ratio of cholesteryl ester to total lipid in large VLDL was associated with future diabetes risk (OR 10.1 comparing individuals in upper vs lower fifth of the multi-metabolite score) in one of the cohorts (mean age 31 years). Conclusions/interpretation: Metabolic biomarkers across multiple molecular pathways are already predictive of the long-term risk of diabetes in young adults. Comprehensive metabolic profiling may help to target preventive interventions for young asymptomatic individuals at increased risk.

Published
2019

7. 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

8. 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

9. 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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10. Circulating amino acids and the risk of macrovascular, microvascular and mortality outcomes in individuals with type 2 diabetes:results from the ADVANCE trial

Author

Welsh, P. (Paul), Rankin, N. (Naomi), Li, Q. (Qiang), Mark, P. B. (Patrick B.), Würtz, P. (Peter), Ala-Korpela, M. (Mika), Marre, M. (Michel), Poulter, N. (Neil), Hamet, P. (Pavel), Chalmers, J. (John), Woodward, M. (Mark), Sattar, N. (Naveed), Welsh, P. (Paul), Rankin, N. (Naomi), Li, Q. (Qiang), Mark, P. B. (Patrick B.), Würtz, P. (Peter), Ala-Korpela, M. (Mika), Marre, M. (Michel), Poulter, N. (Neil), Hamet, P. (Pavel), Chalmers, J. (John), Woodward, M. (Mark), and Sattar, N. (Naveed)

Abstract

Aims/hypotheses We aimed to quantify the association of individual circulating amino acids with macrovascular disease, microvascular disease and all-cause mortality in individuals with type 2 diabetes. Methods: We performed a case-cohort study (N = 3587), including 655 macrovascular events, 342 microvascular events (new or worsening nephropathy or retinopathy) and 632 all-cause mortality events during follow-up, in a secondary analysis of the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) study. For this study, phenylalanine, isoleucine, glutamine, leucine, alanine, tyrosine, histidine and valine were measured in stored plasma samples by proton NMR metabolomics. Hazard ratios were modelled per SD increase in each amino acid. Results: In models investigating associations and potential mechanisms, after adjusting for age, sex and randomised treatment, phenylalanine was positively, and histidine inversely, associated with macrovascular disease risk. These associations were attenuated to the null on further adjustment for extended classical risk factors (including eGFR and urinary albumin/creatinine ratio). After adjustment for extended classical risk factors, higher tyrosine and alanine levels were associated with decreased risk of microvascular disease (HR 0.78; 95% CI 0.67, 0.91 and HR 0.86; 95% CI 0.76, 0.98, respectively). Higher leucine (HR 0.79; 95% CI 0.69, 0.90), histidine (HR 0.89; 95% CI 0.81, 0.99) and valine (HR 0.79; 95% CI 0.70, 0.88) levels were associated with lower risk of mortality. Investigating the predictive ability of amino acids, addition of all amino acids to a risk score modestly improved classification of participants for macrovascular (continuous net reclassification index [NRI] +35.5%, p < 0.001) and microvascular events (continuous NRI +14.4%, p = 0.012). Conclusions/interpretation: We report distinct associations between circulating amino acids and risk of differ

Published
2018

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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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21. 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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