Mika Tiainen

Metabolite Profiling and Cardiovascular Event Risk A Prospective Study of 3 Population-Based Cohorts

Background— High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors. Methods and Results— We applied quantitative nuclear magnetic resonance metabolomics to identify the biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the National Finnish FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the Southall and Brent Revisited (SABRE) study (n=2622; 573 events) and British Women’s Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes mellitus, and medication. When further adjusting for routine lipids, 4 metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation, 1.18; 95% confidence interval, 1.12–1.24; P=4×10–10) and monounsaturated fatty acid levels (1.17; 1.11–1.24; P=1×10–8) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89; 0.84–0.94; P=6×10–5) and docosahexaenoic acid levels (0.90; 0.86–0.95; P=5×10–5) were associated with lower risk. A risk score incorporating these 4 biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the 2 validation cohorts (relative integrated discrimination improvement, 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5% to 10% risk range (net reclassification, 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289). Conclusions— Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.Background— High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors. Methods and Results— We applied quantitative nuclear magnetic resonance metabolomics to identify the biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the National Finnish FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the Southall and Brent Revisited (SABRE) study (n=2622; 573 events) and British Women’s Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P <0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes mellitus, and medication. When further adjusting for routine lipids, 4 metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation, 1.18; 95% confidence interval, 1.12–1.24; P =4×10–10) and monounsaturated fatty acid levels (1.17; 1.11–1.24; P =1×10–8) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89; 0.84–0.94; P =6×10–5) and docosahexaenoic acid levels (0.90; 0.86–0.95; P =5×10–5) were associated with lower risk. A risk score incorporating these 4 biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the 2 validation cohorts (relative integrated discrimination improvement, 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5% to 10% risk range (net reclassification, 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289). Conclusions— Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment. # CLINICAL PERSPECTIVE {#article-title-51}

Circulating Metabolite Predictors of Glycemia in Middle-Aged Men and Women

OBJECTIVE Metabolite predictors of deteriorating glucose tolerance may elucidate the pathogenesis of type 2 diabetes. We investigated associations of circulating metabolites from high-throughput profiling with fasting and postload glycemia cross-sectionally and prospectively on the population level. RESEARCH DESIGN AND METHODS Oral glucose tolerance was assessed in two Finnish, population-based studies consisting of 1,873 individuals (mean age 52 years, 58% women) and reexamined after 6.5 years for 618 individuals in one of the cohorts. Metabolites were quantified by nuclear magnetic resonance spectroscopy from fasting serum samples. Associations were studied by linear regression models adjusted for established risk factors. RESULTS Nineteen circulating metabolites, including amino acids, gluconeogenic substrates, and fatty acid measures, were cross-sectionally associated with fasting and/or postload glucose (P < 0.001). Among these metabolic intermediates, branched-chain amino acids, phenylalanine, and α1-acid glycoprotein were predictors of both fasting and 2-h glucose at 6.5-year follow-up (P < 0.05), whereas alanine, lactate, pyruvate, and tyrosine were uniquely associated with 6.5-year postload glucose (P = 0.003–0.04). None of the fatty acid measures were prospectively associated with glycemia. Changes in fatty acid concentrations were associated with changes in fasting and postload glycemia during follow-up; however, changes in branched-chain amino acids did not follow glucose dynamics, and gluconeogenic substrates only paralleled changes in fasting glucose. CONCLUSIONS Alterations in branched-chain and aromatic amino acid metabolism precede hyperglycemia in the general population. Further, alanine, lactate, and pyruvate were predictive of postchallenge glucose exclusively. These gluconeogenic precursors are potential markers of long-term impaired insulin sensitivity that may relate to attenuated glucose tolerance later in life.

Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA

Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA-targeted intervention to reduce cardiovascular risk.

Metabolomic Profiling of Statin Use and Genetic Inhibition of HMG-CoA Reductase

Background Statins are first-line therapy for cardiovascular disease prevention, but their systemic effects across lipoprotein subclasses, fatty acids, and circulating metabolites remain incompletely characterized. Objectives This study sought to determine the molecular effects of statin therapy on multiple metabolic pathways. Methods Metabolic profiles based on serum nuclear magnetic resonance metabolomics were quantified at 2 time points in 4 population-based cohorts from the United Kingdom and Finland (N = 5,590; 2.5 to 23.0 years of follow-up). Concentration changes in 80 lipid and metabolite measures during follow-up were compared between 716 individuals who started statin therapy and 4,874 persistent nonusers. To further understand the pharmacological effects of statins, we used Mendelian randomization to assess associations of a genetic variant known to mimic inhibition of HMG-CoA reductase (the intended drug target) with the same lipids and metabolites for 27,914 individuals from 8 population-based cohorts. Results Starting statin therapy was associated with numerous lipoprotein and fatty acid changes, including substantial lowering of remnant cholesterol (80% relative to low-density lipoprotein cholesterol [LDL-C]), but only modest lowering of triglycerides (25% relative to LDL-C). Among fatty acids, omega-6 levels decreased the most (68% relative to LDL-C); other fatty acids were only modestly affected. No robust changes were observed for circulating amino acids, ketones, or glycolysis-related metabolites. The intricate metabolic changes associated with statin use closely matched the association pattern with rs12916 in the HMGCR gene (R2 = 0.94, slope 1.00 ± 0.03). Conclusions Statin use leads to extensive lipid changes beyond LDL-C and appears efficacious for lowering remnant cholesterol. Metabolomic profiling, however, suggested minimal effects on amino acids. The results exemplify how detailed metabolic characterization of genetic proxies for drug targets can inform indications, pleiotropic effects, and pharmacological mechanisms.

Lipoprotein subclass metabolism in nonalcoholic steatohepatitis.

Nonalcoholic steatohepatitis (NASH) is associated with increased synthesis of triglycerides and cholesterol coupled with increased VLDL synthesis in the liver. In addition, increased cholesterol content in the liver associates with NASH. Here we study the association of lipoprotein subclass metabolism with NASH. To this aim, liver biopsies from 116 morbidly obese individuals [age 47.3 ± 8.7 (mean ± SD) years, BMI 45.1 ± 6.1 kg/m2, 39 men and 77 women] were used for histological assessment. Proton NMR spectroscopy was used to measure lipid concentrations of 14 lipoprotein subclasses in native serum samples at baseline and after obesity surgery. We observed that total lipid concentration of VLDL and LDL subclasses, but not HDL subclasses, associated with NASH [false discovery rate (FDR) < 0.1]. More specifically, total lipid and cholesterol concentration of VLDL and LDL subclasses associated with inflammation, fibrosis, and cell injury (FDR < 0.1), independent of steatosis. Cholesterol concentration of all VLDL subclasses also correlated with total and free cholesterol content in the liver. All NASH-related changes in lipoprotein subclasses were reversed by obesity surgery. High total lipid and cholesterol concentration of serum VLDL and LDL subclasses are linked to cholesterol accumulation in the liver and to liver cell injury in NASH.

From proton nuclear magnetic resonance spectra to pH. Assessment of 1H NMR pH indicator compound set for deuterium oxide solutions.

In this study, a protocol for pH determination from D(2)O samples using (1)H NMR pH indicator compounds was developed and assessed by exploring the pH-dependency of 13 compounds giving pH-dependent (1)H NMR signals. The indicators cover the pH range from pH(*) 0 to 7.2. Equations to transform the indicator chemical shifts to pH estimates are given here for acetic acid, formic acid, chloroacetic acid, dichloroacetic acid, creatine, creatinine, glycine, histidine, 1,2,4-triazole, and TSP (2,2,3,3-tetradeutero-3-(trimethylsilyl)-propionic acid). To characterize the method in presence of typical solutes, the effects of common metabolites, albumin and ionic strength were also evaluated. For the ionic strengths, the effects were also modelled. The experiments showed that the use of pH sensitive (1)H NMR chemical shifts allows the pH determination of typical metabolite solutions with accuracy of 0.01-0.05 pH units. Also, when the ionic strength is known with accuracy better than 0.1 mol dm(-3) and the solute concentrations are low, pH(nmr)(*) (the NMR estimate of pH) can be assumed to be within 0.05 pH units from potentiometrically determined pH.

Fatty acid metabolism is altered in non-alcoholic steatohepatitis independent of obesity.

BACKGROUND Non-alcoholic steatohepatitis (NASH) is associated with changes in fatty acid (FA) metabolism. However, specific changes in metabolism and hepatic mRNA expression related to NASH independent of simple steatosis, obesity and diet are unknown. METHODS Liver histology, serum and liver FA composition and estimated enzyme activities based on the FA ratios in cholesteryl esters and triglycerides were assessed in 92 obese participants of the Kuopio Obesity Surgery Study (KOBS) divided to those with normal liver, steatosis or NASH (30 men and 62 women, age 46.8±9.5years (mean±SD), BMI 44.2±6.2kg/m(2)). Plasma FA composition was also investigated in the Metabolic Syndrome in Men (METSIM) Study (n=769), in which serum alanine aminotransferase (ALT) was used as a marker of liver disease. RESULTS Obese individuals with NASH had higher activity of estimated activities of delta-6 desaturase (D6D, p<0.002) and stearoyl-CoA desaturase 1 (SCD1, p<0.002) and lower activity of delta-5 desaturase (D5D, p<0.002) when compared to individuals with normal liver. Estimated activities of D5D, D6D and SCD1 correlated positively between liver and serum indicating that serum estimates reflected liver metabolism. Accordingly, NASH was associated with higher hepatic mRNA expression of corresponding genes FADS1, FADS2 and SCD. Finally, differences in FA metabolism that associated with NASH in obese individuals were also associated with high ALT in the METSIM Study. CONCLUSIONS We demonstrated alterations in FA metabolism and endogenous desaturase activities that associate with NASH, independent of obesity and diet. This suggests that changes in endogenous FA metabolism are related to NASH and that they may contribute to the progression of the disease.

Metabolic profiling of alcohol consumption in 9778 young adults

Background: High alcohol consumption is a major cause of morbidity, yet alcohol is associated with both favourable and adverse effects on cardiometabolic risk markers. We aimed to characterize the associations of usual alcohol consumption with a comprehensive systemic metabolite profile in young adults. Methods: Cross-sectional associations of alcohol intake with 86 metabolic measures were assessed for 9778 individuals from three population-based cohorts from Finland (age 24–45 years, 52% women). Metabolic changes associated with change in alcohol intake during 6-year follow-up were further examined for 1466 individuals. Alcohol intake was assessed by questionnaires. Circulating lipids, fatty acids and metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. Results: Increased alcohol intake was associated with cardiometabolic risk markers across multiple metabolic pathways, including higher lipid concentrations in HDL subclasses and smaller LDL particle size, increased proportions of monounsaturated fatty acids and decreased proportion of omega-6 fatty acids, lower concentrations of glutamine and citrate (P < 0.001 for 56 metabolic measures). Many metabolic biomarkers displayed U-shaped associations with alcohol consumption. Results were coherent for men and women, consistent across the three cohorts and similar if adjusting for body mass index, smoking and physical activity. The metabolic changes accompanying change in alcohol intake during follow-up resembled the cross-sectional association pattern (R2 = 0.83, slope = 0.72 ± 0.04). Conclusions: Alcohol consumption is associated with a complex metabolic signature, including aberrations in multiple biomarkers for elevated cardiometabolic risk. The metabolic signature tracks with long-term changes in alcohol consumption. These results elucidate the double-edged effects of alcohol on cardiovascular risk.

Quantitative Quantum Mechanical Spectral Analysis (qQMSA) of 1H NMR spectra of complex mixtures and biofluids

The quantitative interpretation of (1)H NMR spectra of mixtures like the biofluids is a demanding task due to spectral complexity and overlap. Complications may arise also from water suppression, T2-editing, protein interactions, relaxation differences of the species, experimental artifacts and, furthermore, the spectra may contain unknown components and macromolecular background which cannot be easily separated from baseline. In this work, tools and strategies for quantitative Quantum Mechanical Spectral Analysis (qQMSA) of (1)H NMR spectra from complex mixtures were developed and systematically assessed. In the present approach, the signals of well-defined, stoichiometric components are described by a QM model, while the background is described by a multiterm baseline function and the unknown signals using optimizable and adjustable lines, regular multiplets or any spectral structures which can be composed from spectral lines. Any prior knowledge available from the spectrum can also be added to the model. Fitting strategies for weak and strongly overlapping spectral systems were developed and assessed using two basic model systems, the metabolite mixtures without and with macromolecular (serum) background. The analyses show that if the spectra are measured in high-throughput manner, the consistent absolute quantification demands some calibration to compensate the different response factors of the protons and compounds. On the other hand, the results show that also the T2-edited spectra can be measured so that they obey well the QM rules. In general, qQMSA exploits and interprets the spectral information in maximal way taking full advantage from the QM properties of the spectra and, at the same time, offers chemical confidence which means that individual components can be identified with high confidence on the basis of their accurate spectral parameters.

1H NMR spectral analysis and conformational behavior of n-alkanes in different chemical environments

Alkyl chains are common structural units, for example in lipids, and their 1H NMR spectral parameters offer valuable information about their conformational behavior in solvent environment. Even the spectra of short n‐alkanes are complex, which is obviously a reason why their accurate spectral analyses have not been reported before. The present study reports the quantum mechanical analysis of 1H NMR spectra of n‐butane, n‐pentane, n‐hexane, and n‐heptane. The spectral parameters were used to characterize the conformational behavior of n‐alkanes. The temperature dependence analysis of coupling constants suggests that the enthalpy difference between the gauche (g) and trans (t) conformations (ΔHg) of n‐butane in chloroform is 2.55–2.85 kJ mol−1. The difference between the trans–gauche (tg) and all‐trans (tt) conformers of n‐pentane (ΔHtg) seems to be 0.1–0.2 kJ mol−1 higher. The coupling constant information shows that the tn conformations become more favored with longer chains, although not only for energetic reasons but also partly because the g+g‐ arrangements become sterically unfavorable, which decreases the number of favorable gn‐type conformations. The analysis of the 1H NMR spectra of n‐pentane and n‐hexane in solvents representing different chemical environments indicates that polar and spherical dimethyl sulfoxide favors clearly the g conformations, whereas n‐hexane‐d14 favors slightly the extended tn conformation. In addition to the intrinsic scientific importance for NMR spectral parameter prediction and molecular modeling in solution, the results provide some insights to behavior of hydrocarbon chains and their spectra in different chemical environments. Copyright