Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank.

Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.

Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation.

Pleiotropy and genetic correlation are widespread features in genome-wide association studies (GWAS), but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.

Effects of general and central adiposity on circulating lipoprotein, lipid, and metabolite levels in UK Biobank: A multivariable Mendelian randomization study.

Background: The direct effects of general adiposity (body mass index (BMI)) and central adiposity (waist-to-hip-ratio (WHR)) on circulating lipoproteins, lipids, and metabolites are unknown. Methods: We used new metabolic data from UK Biobank (N=109,532, a five-fold higher N over previous studies). EDTA-plasma was used to quantify 249 traits with nuclear-magnetic-resonance spectroscopy including subclass-specific lipoprotein concentrations and lipid content, plus pre-glycemic and inflammatory metabolites. We used univariable and multivariable two-stage least-squares regression models with genetic risk scores for BMI and WHR as instruments to estimate total (unadjusted) and direct (mutually-adjusted) effects of BMI and WHR on metabolic traits; plus effects on statin use and interaction by sex, statin use, and age (proxy for medication use). Findings: Higher BMI decreased apolipoprotein B and low-density lipoprotein cholesterol (LDL-C) before and after WHR-adjustment, whilst BMI increased triglycerides only before WHR-adjustment. These effects of WHR were larger and BMI-independent. Direct effects differed markedly by sex, e.g., triglycerides increased only with BMI among men, and only with WHR among women. Adiposity measures increased statin use and showed metabolic effects which differed by statin use and age. Among the youngest (38-53y, statins-5%), BMI and WHR (per-SD) increased LDL-C (total effects: 0.04-SD, 95%CI=-0.01,0.08 and 0.10-SD, 95%CI=0.02,0.17 respectively), but only WHR directly. Among the oldest (63-73y, statins-29%), BMI and WHR directly lowered LDL-C (-0.19-SD, 95%CI=-0.27,-0.11 and -0.05-SD, 95%CI=-0.16,0.06 respectively). Interpretation: Excess adiposity likely raises atherogenic lipid and metabolite levels exclusively via adiposity stored centrally, particularly among women. Apparent effects of adiposity on lowering LDL-C are likely explained by an effect of adiposity on statin use. Funding: UK Medical Research Council; British Heart Foundation; Novo Nordisk; National Institute for Health Research; Wellcome Trust; Cancer Research UK.

Circulating Metabolic Biomarkers Are Consistently Associated With Type 2 Diabetes Risk in Asian and European Populations.

CONTEXT: While Asians have a higher risk of type 2 diabetes (T2D) than Europeans for a given body mass index (BMI), it remains unclear whether the same markers of metabolic pathways are associated with diabetes. OBJECTIVE: We evaluated associations between metabolic biomarkers and incidence of T2D in 3 major Asian ethnic groups (Chinese, Malay, and Indian) and a European population. METHODS: We analyzed data from adult males and females of 2 cohorts from Singapore (n = 6393) consisting of Chinese, Malays, and Indians and 3 cohorts of European-origin participants from Finland (n = 14 558). We used nuclear magnetic resonance to quantify 154 circulating metabolic biomarkers at baseline and performed logistic regression to assess associations with T2D risk adjusted for age, sex, BMI and glycemic markers. RESULTS: Of the 154 metabolic biomarkers, 59 were associated with higher risk of T2D in both Asians and Europeans (P < 0.0003, Bonferroni-corrected). These included branched chain and aromatic amino acids, the inflammatory marker glycoprotein acetyls, total fatty acids, monounsaturated fatty acids, apolipoprotein B, larger very low-density lipoprotein particle sizes, and triglycerides. In addition, 13 metabolites were associated with a lower T2D risk in both populations, including omega-6 polyunsaturated fatty acids and larger high-density lipoprotein particle sizes. Associations were consistent within the Asian ethnic groups (all Phet ≥ 0.05) and largely consistent for the Asian and European populations (Phet ≥ 0.05 for 128 of 154 metabolic biomarkers). CONCLUSION: Metabolic biomarkers across several biological pathways were consistently associated with T2D risk in Asians and Europeans.

Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes.

AIMS/HYPOTHESIS: This prospective, observational study examines associations between 51 urinary metabolites and risk of progression of diabetic nephropathy in individuals with type 1 diabetes by employing an automated NMR metabolomics technique suitable for large-scale urine sample collections. METHODS: We collected 24-h urine samples for 2670 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study and measured metabolite concentrations by NMR. Individuals were followed up for 9.0 ± 5.0 years until their first sign of progression of diabetic nephropathy, end-stage kidney disease or study end. Cox regressions were performed on the entire study population (overall progression), on 1999 individuals with normoalbuminuria and 347 individuals with macroalbuminuria at baseline. RESULTS: Seven urinary metabolites were associated with overall progression after adjustment for baseline albuminuria and chronic kidney disease stage (p < 8 × 10-4): leucine (HR 1.47 [95% CI 1.30, 1.66] per 1-SD creatinine-scaled metabolite concentration), valine (1.38 [1.22, 1.56]), isoleucine (1.33 [1.18, 1.50]), pseudouridine (1.25 [1.11, 1.42]), threonine (1.27 [1.11, 1.46]) and citrate (0.84 [0.75, 0.93]). 2-Hydroxyisobutyrate was associated with overall progression (1.30 [1.16, 1.45]) and also progression from normoalbuminuria (1.56 [1.25, 1.95]). Six amino acids and pyroglutamate were associated with progression from macroalbuminuria. CONCLUSIONS/INTERPRETATION: Branched-chain amino acids and other urinary metabolites were associated with the progression of diabetic nephropathy on top of baseline albuminuria and chronic kidney disease. We found differences in associations for overall progression and progression from normo- and macroalbuminuria. These novel discoveries illustrate the utility of analysing urinary metabolites in entire population cohorts.

Comprehensive biomarker profiling of hypertension in 36 985 Finnish individuals.

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 ±â€Š14.2; 53.1% women) and longitudinal (N = 4197; age 49.4 ±â€Š11.8, 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. CONCLUSION: 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.

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.

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<0.001). In contrast, triglyceride levels in low-density lipoprotein and high-density lipoprotein were associated with both end points (Pheterogeneity>0.05). Lower proportion of polyunsaturated fatty acids relative to total fatty acids, and higher concentrations of monounsaturated fatty acids, glycolysis-related metabolites, and inflammatory protein markers were strongly associated with incident PAD, and many of these associations were stronger for PAD than for CAD (Pheterogeneity<0.001). Most differences in metabolic profiles for PAD and CAD remained when adjusting for traditional risk factors. Conclusions The metabolic biomarker profile for future PAD risk is distinct from that of CAD. This may represent pathophysiological differences.

Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population.

Biomarkers of low-grade inflammation have been associated with susceptibility to a severe infectious disease course, even when measured prior to disease onset. We investigated whether metabolic biomarkers measured by nuclear magnetic resonance (NMR) spectroscopy could be associated with susceptibility to severe pneumonia (2507 hospitalised or fatal cases) and severe COVID-19 (652 hospitalised cases) in 105,146 generally healthy individuals from UK Biobank, with blood samples collected 2007-2010. The overall signature of metabolic biomarker associations was similar for the risk of severe pneumonia and severe COVID-19. A multi-biomarker score, comprised of 25 proteins, fatty acids, amino acids, and lipids, was associated equally strongly with enhanced susceptibility to severe COVID-19 (odds ratio 2.9 [95%CI 2.1-3.8] for highest vs lowest quintile) and severe pneumonia events occurring 7-11 years after blood sampling (2.6 [1.7-3.9]). However, the risk for severe pneumonia occurring during the first 2 years after blood sampling for people with elevated levels of the multi-biomarker score was over four times higher than for long-term risk (8.0 [4.1-15.6]). If these hypothesis generating findings on increased susceptibility to severe pneumonia during the first few years after blood sampling extend to severe COVID-19, metabolic biomarker profiling could potentially complement existing tools for identifying individuals at high risk. These results provide novel molecular understanding on how metabolic biomarkers reflect the susceptibility to severe COVID-19 and other infections in the general population.

National policies for mitigating the COVID-19 pandemic include stricter measures for people considered to be at high risk of severe and potentially fatal cases of the disease. Although older age and pre-existing health conditions are strong risk factors, it is poorly understood why susceptibility varies so widely in the population. People with cardiometabolic diseases, such as diabetes and liver diseases, or chronic inflammation are at higher risk of severe COVID-19 and other infections including pneumonia. These conditions alter the molecules circulating in the blood, providing potential 'biomarkers' to determine whether a person is more likely to develop a fatal infection. Uncovering these blood biomarkers could help to identify people who are prone to life-threatening infections despite not having ever been diagnosed with a cardiometabolic disease. To find these biomarkers, Julkunen et al. studied blood samples that had been collected from 105,000 healthy individuals in the United Kingdom over ten years ago. The data showed that individuals with biomarkers linked to low-grade inflammation and cardiometabolic disease were more likely to have died or been hospitalised with pneumonia. A score based on 25 of these biomarkers provided the best predictor of severe pneumonia. This biomarker score performed up to four times better within the first few years after blood sampling compared to predicting cases of pneumonia a decade later. The same blood biomarker changes were also linked with developing severe COVID-19 over ten years after the blood samples had been collected. The predictive value of the biomarker score was similar for both severe COVID-19 and the long-term risk of severe pneumonia. Julkunen et al. propose that the metabolic biomarkers reflect inhibited immunity that impairs response to infections. The results from over 100,000 individuals suggest that these blood biomarkers may help to identify people at high risk of severe COVID-19 or other infectious diseases.

Plasma fatty acids and the risk of vascular disease and mortality outcomes in individuals with type 2 diabetes: results from the ADVANCE study.

AIMS/HYPOTHESIS: This biomarker study aimed to quantify the association of essential and other plasma fatty acid biomarkers with macrovascular disease, microvascular disease and death in individuals with type 2 diabetes. METHODS: A case-cohort study (N = 3576), including 654 macrovascular events, 341 microvascular events and 631 deaths during 5 years of (median) follow-up, was undertaken as a secondary analysis of the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified-Release Controlled Evaluation (ADVANCE) study (full details of the study design and primary endpoints of the ADVANCE trial and its case-cohort have been published previously). This current study considers new data: fatty acids measured from baseline plasma samples by proton NMR analysis. The fatty acids measured were n-3, docosahexaenoic acid (DHA), n-6, linoleic acid, and polyunsaturated, monounsaturated and saturated fatty acids. HRs were modelled per SD higher (percentage) fatty acid. C statistics and continuous net reclassification improvement were used to test the added value of fatty acids compared with traditional cardiovascular risk factors. RESULTS: After adjustment for traditional cardiovascular risk factors, an inverse association was observed for n-3 fatty acids and DHA with the risk of macrovascular events (HR [95% CI]: 0.87 [0.80, 0.95] and 0.88 [0.81, 0.96], respectively, per 1 SD higher percentage), and for n-3 fatty acids with the risk of death (HR 0.91 [95% CI 0.84, 0.99] per 1 SD higher percentage). Such associations were also evident when investigating absolute levels of fatty acids. There were no statistically significant associations between any fatty acids and microvascular disease after adjustment. However, there was limited improvement in the predictive ability of models when any fatty acid was added. CONCLUSIONS/INTERPRETATION: Plasma n-3 fatty acids and DHA were found to be inversely associated with macrovascular disease, while n-3 fatty acids were also inversely associated with death. These results support the cardioprotective effects of n-3 fatty acids and DHA and further merit testing the role of high-dose supplementation with n-3 fatty acids in individuals with type 2 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT00145925. Graphical abstract.

Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy.

Cytokines are essential regulatory components of the immune system, and their aberrant levels have been linked to many disease states. Despite increasing evidence that cytokines operate in concert, many of the physiological interactions between cytokines, and the shared genetic architecture that underlies them, remain unknown. Here, we aimed to identify and characterize genetic variants with pleiotropic effects on cytokines. Using three population-based cohorts (n = 9,263), we performed multivariate genome-wide association studies (GWAS) for a correlation network of 11 circulating cytokines, then combined our results in meta-analysis. We identified a total of eight loci significantly associated with the cytokine network, of which two (PDGFRB and ABO) had not been detected previously. In addition, conditional analyses revealed a further four secondary signals at three known cytokine loci. Integration, through the use of Bayesian colocalization analysis, of publicly available GWAS summary statistics with the cytokine network associations revealed shared causal variants between the eight cytokine loci and other traits; in particular, cytokine network variants at the ABO, SERPINE2, and ZFPM2 loci showed pleiotropic effects on the production of immune-related proteins, on metabolic traits such as lipoprotein and lipid levels, on blood-cell-related traits such as platelet count, and on disease traits such as coronary artery disease and type 2 diabetes.

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

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.

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

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.

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

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.

Metabolomics: population epidemiology and concordance in Australian children aged 11-12 years and their parents.

OBJECTIVES: Nuclear magnetic resonance (NMR) metabolomics is high throughput and cost-effective, with the potential to improve the understanding of disease and risk. We examine the circulating metabolic profile by quantitative NMR metabolomics of a sample of Australian 11-12 year olds children and their parents, describe differences by age and sex, and explore the correlation of metabolites in parent-child dyads. DESIGN: The population-based cross-sectional Child Health CheckPoint study nested within the Longitudinal Study of Australian Children. SETTING: Blood samples collected from CheckPoint participants at assessment centres in seven Australian cities and eight regional towns; February 2015-March 2016. PARTICIPANTS: 1180 children and 1325 parents provided a blood sample and had metabolomics data available. This included 1133 parent-child dyads (518 mother-daughter, 469 mother-son, 68 father-daughter and 78 father-son). OUTCOME MEASURES: 228 metabolic measures were obtained for each participant. We focused on 74 biomarkers including amino acid species, lipoprotein subclass measures, lipids, fatty acids, measures related to fatty acid saturation, and composite markers of inflammation and energy homeostasis. RESULTS: We identified differences in the concentration of specific metabolites between childhood and adulthood and in metabolic profiles in children and adults by sex. In general, metabolite concentrations were higher in adults than children and sex differences were larger in adults than in children. Positive correlations were observed for the majority of metabolites including isoleucine (CC 0.33, 95% CI 0.27 to 0.38), total cholesterol (CC 0.30, 95% CI 0.24 to 0.35) and omega 6 fatty acids (CC 0.28, 95% CI 0.23 to 0.34) in parent-child comparisons. CONCLUSIONS: We describe the serum metabolite profiles from mid-childhood and adulthood in a population-based sample, together with a parent-child concordance. Differences in profiles by age and sex were observed. These data will be informative for investigation of the childhood origins of adult non-communicable diseases and for comparative studies in other populations.

Metabolomic Signature of Angiopoietin-Like Protein 3 Deficiency in Fasting and Postprandial State.

Objective- Loss-of-function (LOF) variants in the ANGPTL3 (angiopoietin-like protein 3) have been associated with low levels of plasma lipoproteins and decreased coronary artery disease risk. We aimed to determine detailed metabolic effects of genetically induced ANGPTL3 deficiency in fasting and postprandial state. Approach and Results- We studied individuals carrying S17X LOF mutation in ANGPTL3 (6 homozygous and 32 heterozygous carriers) and 38 noncarriers. Nuclear magnetic resonance metabolomics was used to quantify 225 circulating metabolic measures. We compared metabolic differences between LOF carriers and noncarriers in fasting state and after a high-fat meal. In fasting, ANGPTL3 deficiency was characterized by similar extent of reductions in LDL (low-density lipoprotein) cholesterol (0.74 SD units lower concentration per LOF allele [95% CI, 0.42-1.06]) as observed for many TRL (triglyceride-rich lipoprotein) measures, including VLDL (very-low-density lipoprotein) cholesterol (0.75 [95% CI, 0.45-1.05]). Within most lipoprotein subclasses, absolute levels of cholesterol were decreased more than triglycerides, resulting in the relative proportion of cholesterol being reduced within TRLs and their remnants. Further, ß-hydroxybutyrate was elevated (0.55 [95% CI, 0.21-0.89]). Homozygous ANGPTL3 LOF carriers showed essentially no postprandial increase in TRLs and fatty acids, without evidence for adverse compensatory metabolic effects. Conclusions- In addition to overall triglyceride- and LDL cholesterol-lowering effects, ANGPTL3 deficiency results in reduction of cholesterol proportion within TRLs and their remnants. Further, ANGPTL3 LOF carriers had elevated ketone body production, suggesting enhanced hepatic fatty acid ß-oxidation. The detailed metabolic profile in human knockouts of ANGPTL3 reinforces inactivation of ANGPTL3 as a promising therapeutic target for decreasing cardiovascular risk.

Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study.

BACKGROUND: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer-metabolite associations using two-sample Mendelian randomization (MR). METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

Mendelian randomization reveals unexpected effects of CETP on the lipoprotein profile.

According to the current dogma, cholesteryl ester transfer protein (CETP) decreases high-density lipoprotein (HDL)-cholesterol (C) and increases low-density lipoprotein (LDL)-C. However, detailed insight into the effects of CETP on lipoprotein subclasses is lacking. Therefore, we used a Mendelian randomization approach based on a genetic score for serum CETP concentration (rs247616, rs12720922 and rs1968905) to estimate causal effects per unit (µg/mL) increase in CETP on 159 standardized metabolic biomarkers, primarily lipoprotein subclasses. Metabolic biomarkers were measured by nuclear magnetic resonance (NMR) in 5672 participants of the Netherlands Epidemiology of Obesity (NEO) study. Higher CETP concentrations were associated with less large HDL (largest effect XL-HDL-C, P = 6 × 10-22) and more small VLDL components (largest effect S-VLDL cholesteryl esters, P = 6 × 10-6). No causal effects were observed with LDL subclasses. All these effects were replicated in an independent cohort from European ancestry (MAGNETIC NMR GWAS; n ~20,000). Additionally, we assessed observational associations between ELISA-measured CETP concentration and metabolic measures. In contrast to results from Mendelian randomization, observationally, CETP concentration predominantly associated with more VLDL, IDL and LDL components. Our results show that CETP is an important causal determinant of HDL and VLDL concentration and composition, which may imply that the CETP inhibitor anacetrapib decreased cardiovascular disease risk through specific reduction of small VLDL rather than LDL. The contrast between genetic and observational associations might be explained by a high capacity of VLDL, IDL and LDL subclasses to carry CETP, thereby concealing causal effects on HDL.

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

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 [ß (standard deviation difference vs. placebo): 0.69; 95% CI = 0.24, 1.15; p = 0.003], valine (ß: -0.62; -1.03, -0.02; p = 0.004), pyruvate (ß: -0.56; -0.95, -0.16; p = 0.006) and docosahexaenoic acid (ß: -0.50; -085, -0.14; p = 0.006). Valine and diacylglycerol were lower in the lycopene dietary advice group (ß: -0.65; -1.04, -0.26; p = 0.001 and ß: -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.

Biomarker Glycoprotein Acetyls Is Associated With the Risk of a Wide Spectrum of Incident Diseases and Stratifies Mortality Risk in Angiography Patients.

BACKGROUND: Integration of systems-level biomolecular information with electronic health records has led to recent interest in the glycoprotein acetyls (GlycA) biomarker-a serum- or plasma-derived nuclear magnetic resonance spectroscopy signal that represents the abundance of circulating glycated proteins. GlycA predicts risk of diverse outcomes, including cardiovascular disease, type 2 diabetes mellitus, and all-cause mortality; however, the underlying detailed associations of GlycA's morbidity and mortality risk are currently unknown. METHODS: We used 2 population-based cohorts totaling 11 861 adults from the Finnish general population to test for an association with 468 common incident hospitalization and mortality outcomes during an 8-year follow-up. Further, we utilized 900 angiography patients to test for GlycA association with mortality risk and potential utility for mortality risk discrimination during 12-year follow-up. RESULTS: New associations with GlycA and incident alcoholic liver disease, chronic renal failure, glomerular diseases, chronic obstructive pulmonary disease, inflammatory polyarthropathies, and hypertension were uncovered, and known incident disease associations were replicated. GlycA associations for incident disease outcomes were in general not attenuated when adjusting for hsCRP (high-sensitivity C-reactive protein). Among 900 patients referred to angiography, GlycA had hazard ratios of 4.87 (95% CI, 2.45-9.65) and 5.00 (95% CI, 2.38-10.48) for 12-year risk of mortality in the fourth and fifth quintiles by GlycA levels, demonstrating its prognostic potential for identification of high-risk individuals. When modeled together, both hsCRP and GlycA were attenuated but remained significant. CONCLUSIONS: GlycA was predictive of myriad incident diseases across many major internal organs and stratified mortality risk in angiography patients. Both GlycA and hsCRP had shared and independent contributions to mortality risk, suggesting chronic inflammation as an etiological factor. GlycA may be useful in improving risk prediction in specific disease settings.