Tuulia Tynkkynen

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}

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.

A multi-metabolite analysis of serum by 1H NMR spectroscopy: early systemic signs of Alzheimer's disease.

A three-molecular-window approach for (1)H NMR spectroscopy of serum is presented to obtain specific molecular data on lipoproteins, various low-molecular-weight metabolites, and individual lipid molecules together with their degree of (poly)(un)saturation. The multiple data were analysed with self-organising maps, illustrating the strength of the approach as a holistic metabonomics framework in solely data-driven metabolic phenotyping. We studied 180 serum samples of which 30% were related to mild cognitive impairment (MCI), a neuropsychological diagnosis with severely increased risk for Alzheimers disease (AD). The results underline the association between MCI and the metabolic syndrome (MetS). Additionally, the low relativeamount of omega-3 fatty acids appears more indicative of MCI than low serum omega-3 or polyunsaturated fatty acid concentration as such. The analyses also feature the role of elevated glycoproteins in the risk for AD, supporting the view that coexistence of inflammation and the MetS forms a high risk condition for cognitive decline.

High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis

AIMS High-throughput metabolite quantification holds promise for cardiovascular risk assessment. Here, we evaluated whether metabolite quantification by nuclear magnetic resonance (NMR) improves prediction of subclinical atherosclerosis in comparison to conventional lipid testing. METHODS AND RESULTS Circulating lipids, lipoprotein subclasses, and small molecules were assayed by NMR for 1595 individuals aged 24-39 years from the population-based Cardiovascular Risk in Young Finns Study. Carotid intima-media thickness (IMT), a marker of subclinical atherosclerosis, was measured in 2001 and 2007. Baseline conventional risk factors and systemic metabolites were used to predict 6-year incidence of high IMT (≥ 90 th percentile) or plaque. The best prediction of high intima-media thickness was achieved when total and HDL cholesterol were replaced by NMR-determined LDL cholesterol and medium HDL, docosahexaenoic acid, and tyrosine in prediction models with risk factors from the Framingham risk score. The extended prediction model improved risk stratification beyond established risk factors alone; area under the receiver operating characteristic curve 0.764 vs. 0.737, P =0.02, and net reclassification index 17.6%, P =0.0008. Higher docosahexaenoic acid levels were associated with decreased risk for incident high IMT (odds ratio: 0.74; 95% confidence interval: 0.67-0.98; P = 0.007). Tyrosine (1.33; 1.10-1.60; P = 0.003) and glutamine (1.38; 1.13-1.68; P = 0.001) levels were associated with 6-year incident high IMT independent of lipid measures. Furthermore, these amino acids were cross-sectionally associated with carotid IMT and the presence of angiographically ascertained coronary artery disease in independent populations. CONCLUSION High-throughput metabolite quantification, with new systemic biomarkers, improved risk stratification for subclinical atherosclerosis in comparison to conventional lipids and could potentially be useful for early cardiovascular risk assessment.

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.

Metabolic diversity of progressive kidney disease in 325 patients with type 1 diabetes (the FinnDiane Study).

Type 1 diabetic patients with varying severity of kidney disease were investigated to create multimetabolite models of the disease process. Urinary albumin excretion rate was measured for 3358 patients with type 1 diabetes. Prospective records were available for 1051 patients, of whom 163 showed progression of albuminuria (8.3-year follow-up), and 162 were selected as stable controls. At baseline, serum lipids, lipoprotein subclasses, and low-molecular weight metabolites were quantified by NMR spectroscopy (325 samples). The data were analyzed by the self-organizing map. In cross-sectional analyses, patients with no complications had low serum lipids, less inflammation, and better glycemic control, whereas patients with advanced kidney disease had high serum cystatin-C and sphingomyelin. These phenotype extremes shared low unsaturated fatty acids (UFAs) and phospholipids. Prospectively, progressive albuminuria was associated with high UFAs, phospholipids, and IDL and LDL lipids. Progression at longer duration was associated with high HDL lipids, whereas earlier progression was associated with poor glycemic control, increased saturated fatty acids (SFAs), and inflammation. Diabetic kidney disease consists of diverse metabolic phenotypes: UFAs, phospholipids, IDL, and LDL may be important in the subclinical phase, high SFAs and low HDL suggest accelerated progression, and the sphingolipid pathway in advanced kidney injury deserves further research.

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.

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.

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

Effects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence

Background: Hormonal contraception is commonly used worldwide, but its systemic effects across lipoprotein subclasses, fatty acids, circulating metabolites and cytokines remain poorly understood. Methods: A comprehensive molecular profile (75 metabolic measures and 37 cytokines) was measured for up to 5841 women (age range 24–49 years) from three population-based cohorts. Women using combined oral contraceptive pills (COCPs) or progestin-only contraceptives (POCs) were compared with those who did not use hormonal contraception. Metabolomics profiles were reassessed for 869 women after 6 years to uncover the metabolic effects of starting, stopping and persistently using hormonal contraception. Results: The comprehensive molecular profiling allowed multiple new findings on the metabolic associations with the use of COCPs. They were positively associated with lipoprotein subclasses, including all high-density lipoprotein (HDL) subclasses. The associations with fatty acids and amino acids were strong and variable in direction. COCP use was negatively associated with albumin and positively associated with creatinine and inflammatory markers, including glycoprotein acetyls and several growth factors and interleukins. Our findings also confirmed previous results e.g. for increased circulating triglycerides and HDL cholesterol. Starting COCPs caused similar metabolic changes to those observed cross-sectionally: the changes were maintained in consistent users and normalized in those who stopped using. In contrast, POCs were only weakly associated with metabolic and inflammatory markers. Results were consistent across all cohorts and for different COCP preparations and different types of POC delivery. Conclusions: Use of COCPs causes widespread metabolic and inflammatory effects. However, persistent use does not appear to accumulate the effects over time and the metabolic perturbations are reversed upon discontinuation. POCs have little effect on systemic metabolism and inflammation.