Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

Metabolomic profiling for dyslipidemia in pediatric patients with sickle cell disease, on behalf of the IHCC consortium

BACKGROUND

Previous study has shown that dyslipidemia is common in patients with Sickle cell disease (SCD) and is associated with more serious SCD complications.

METHODS

This study investigated systematically dyslipidemia in SCD using a state-of-art nuclear magnetic resonance (NMR) metabolomics platform, including 147 pediatric cases with SCD and 1234 controls without SCD. We examined 249 metabolomic biomarkers, including 98 biomarkers for lipoprotein subclasses, 70 biomarkers for relative lipoprotein lipid concentrations, plus biomarkers for fatty acids and phospholipids.

RESULTS

Specific patterns of hypolipoproteinemia and hypocholesterolemia in pediatric SCD were observed in lipoprotein subclasses other than larger VLDL subclasses. Triglycerides are not significantly changed in SCD, except increased relative concentrations in lipoprotein subclasses. Decreased plasma FFAs (including total-FA, SFA, PUFA, Omega-6, and linoleic acid) and decreased plasma phospholipids were observed in SCD.

CONCLUSION

This study scrutinized, for the first time, lipoprotein subclasses in pediatric patients with SCD, and identified SCD-specific dyslipidemia from altered lipoprotein metabolism. The findings of this study depict a broad panorama of lipid metabolism and nutrition in SCD, suggesting the potential of specific dietary supplementation of the deficient nutrients for the management of SCD.

GlycA, a Biomarker of Low-Grade Inflammation, Is Increased in Male Night Shift Workers

Sustained night shift work is associated with various adverse health risks, including an increased risk of cardiovascular disease, type II diabetes, and susceptibility to infectious respiratory diseases. The extent of these adverse health effects, however, seems to greatly vary between night shift workers, yet the underlying reasons and the mechanisms underlying these interindividual differences remain poorly understood. Metabolomics assays in the blood have recently gained much attention as a minimally invasive biomarker platform capturing information predictive of metabolic and cardiovascular diseases. In this cross-sectional study, we explored and compared the metabolic profiles of 1010 night shift workers and 1010 age- and sex-matched day workers (non-shift workers) from the Lifelines Cohort Study. The metabolic profiles were determined using the ¹H-NMR Nightingale platform for the quantification of 250 parameters of metabolism, including routine lipids, extensive lipoprotein subclasses, fatty acid composition, and various low-molecular metabolites, including amino acids, ketone bodies, and gluconeogenesis-related metabolites. Night shift workers had an increased BMI (26.6 vs. 25.9 kg/m²) compared with day workers (non-shift workers) in both sexes, were slightly more likely to be ever smokers (only in males) (54% vs. 46%), worked on average 5.9 ± 3.7 night shifts per month, and had been working in night shifts for 18.3 ± 10.5 years on average. We observed changes in several metabolic markers in male night shift workers compared with non-shift workers, but no changes were observed in women. In men, we observed higher levels of glycoprotein acetyls (GlycA), triglycerides, and fatty acids compared with non-shift workers. The changes were seen in the ratio of triglycerides and cholesterol(esters) to total lipids in different sizes of VLDL particles. Glycoprotein acetyls (GlycAs) are of particular interest as markers since they are known as biomarkers for low-grade chronic inflammation. When the analyses were adjusted for BMI, no significant associations were observed. Further studies are needed to better understand the relationship between night shift work and metabolic profiles, particularly with respect to the role of sex and BMI in this relationship.

Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations

BACKGROUND

Dyslipidemia is a major risk factor for cardiovascular disease, and diabetes impacts the lipid metabolism through multiple pathways. In addition to the standard lipid measurements, apolipoprotein concentrations provide added awareness of the burden of circulating lipoproteins. While common genetic variants modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesterolemia and other genetic disorders of lipid metabolism. We aimed to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits.

METHODS

We analyzed whole-exome (WES) and whole-genome sequencing (WGS) data of 481 and 474 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 79 serum lipid and apolipoprotein phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance spectroscopy.

RESULTS

The single-variant analysis identified an association between the LIPC p.Thr405Met (rs113298164) and serum apolipoprotein A1 concentrations (p=7.8×10-8). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, GTF3C5, MARCHF10, and RYR3 (p<2.9×10-6). The RBM47 gene is required for apolipoprotein B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III concentrations was due to a rare 21 base pair p.Ala496-Ala502 deletion; in replication, the burden of rare deleterious variants in RBM47 was associated with lower triglyceride concentrations in WES of >170,000 individuals from multiple ancestries (p=0.0013). Two PAVs in GTF3C5 were highly enriched in the Finnish population and associated with cardiovascular phenotypes in the general population. In the previously known APOB gene, we identified novel associations at two protein-truncating variants resulting in lower serum non-HDL cholesterol (p=4.8×10-4), apolipoprotein B (p=5.6×10-4), and LDL cholesterol (p=9.5×10-4) concentrations.

CONCLUSIONS

We identified lipid and apolipoprotein-associated variants in the previously known LIPC and APOB genes, as well as PAVs in GTF3C5 associated with LDLC, and in RBM47 associated with apolipoprotein C-III concentrations, implicated as an independent CVD risk factor. Identification of rare loss-of-function variants has previously revealed genes that can be targeted to prevent CVD, such as the LDL cholesterol-lowering loss-of-function variants in the PCSK9 gene. Thus, this study suggests novel putative therapeutic targets for the prevention of CVD.

Plasma metabolomic profiling of dietary patterns associated with glucose metabolism status: The Maastricht Study

BACKGROUND

Glucose metabolism has been reported to be affected by dietary patterns, while the underlying mechanisms involved remain unclear. This study aimed to investigate the potential mediation role of circulating metabolites in relation to dietary patterns for prediabetes and type 2 diabetes.

METHODS

Data was derived from The Maastricht Study that comprised of 3441 participants (mean age of 60 years) with 28% type 2 diabetes patients by design. Dietary patterns were assessed using a validated food frequency questionnaire (FFQ), and the glucose metabolism status (GMS) was defined according to WHO guidelines. Both cross-sectional and prospective analyses were performed for the circulating metabolome to investigate their associations and mediations with responses to dietary patterns and GMS.

RESULTS

Among 226 eligible metabolite measures obtained from targeted metabolomics, 14 were identified to be associated and mediated with three dietary patterns (i.e. Mediterranean Diet (MED), Dietary Approaches to Stop Hypertension Diet (DASH), and Dutch Healthy Diet (DHD)) and overall GMS. Of these, the mediation effects of 5 metabolite measures were consistent for all three dietary patterns and GMS. Based on a 7-year follow-up, a decreased risk for apolipoprotein A1 (APOA1) and docosahexaenoic acid (DHA) (RR 0.60, 95% CI 0.55, 0.65; RR 0.89, 95% CI 0.83, 0.97, respectively) but an increased risk for ratio of ω-6 to ω-3 fatty acids (RR 1.29, 95% CI 1.05, 1.43) of type 2 diabetes were observed from prediabetes, while APOA1 showed a decreased risk of type 2 diabetes from normal glucose metabolism (NGM; RR 0.82, 95% CI 0.75, 0.89).

CONCLUSIONS

In summary, this study suggests that adherence to a healthy dietary pattern (i.e. MED, DASH, or DHD) could affect the GMS through circulating metabolites, which provides novel insights into understanding the biological mechanisms of diet on glucose metabolism and leads to facilitating prevention strategy for type 2 diabetes.

Metabolomic profiles predict individual multidisease outcomes

Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.

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.

Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories-a UK Biobank Random Forest classification study

Aging has often been characterized by progressive cognitive decline in memory and especially executive function. Yet some adults, aged 80 years or older, are "super-agers" that exhibit cognitive performance like younger adults. It is unknown if there are adults in mid-life with similar superior cognitive performance ("positive-aging") versus cognitive decline over time and if there are blood biomarkers that can distinguish between these groups. Among 1303 participants in UK Biobank, latent growth curve models classified participants into different cognitive groups based on longitudinal fluid intelligence (FI) scores over 7-9 years. Random Forest (RF) classification was then used to predict cognitive trajectory types using longitudinal predictors including demographic, vascular, bioenergetic, and immune factors. Feature ranking importance and performance metrics of the model were reported. Despite model complexity, we achieved a precision of 77% when determining who would be in the "positive-aging" group (n = 563) vs. cognitive decline group (n = 380). Among the top fifteen features, an equal number were related to either vascular health or cellular bioenergetics but not demographics like age, sex, or socioeconomic status. Sensitivity analyses showed worse model results when combining a cognitive maintainer group (n = 360) with the positive-aging or cognitive decline group. Our results suggest that optimal cognitive aging may not be related to age per se but biological factors that may be amenable to lifestyle or pharmacological changes.

The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes

Associations of circulating metabolic biomarkers with type 2 diabetes (T2D) and their added value for risk prediction are uncertain among Chinese adults. A case-cohort study included 882 T2D cases diagnosed during 8-years' follow-up and a subcohort of 789 participants. NMR-metabolomic profiling quantified 225 plasma biomarkers in stored samples taken at recruitment into the study. Cox regression yielded adjusted hazard ratios (HRs) for T2D associated with individual biomarkers, with a set of biomarkers incorporated into an established T2D risk prediction model to assess improvement in discriminatory ability. Mean baseline BMI (SD) was higher in T2D cases than in the subcohort (25.7 [3.6] vs. 23.9 [3.6] kg/m²). Overall, 163 biomarkers were significantly and independently associated with T2D at false discovery rate (FDR) controlled p < 0.05, and 138 at FDR-controlled p < 0.01. Branched chain amino acids (BCAA), apolipoprotein B/apolipoprotein A1, triglycerides in VLDL and medium and small HDL particles, and VLDL particle size were strongly positively associated with T2D (HRs 1.74-2.36 per 1 SD, p < 0.001). HDL particle size, cholesterol concentration in larger HDL particles and docosahexaenoic acid levels were strongly inversely associated with T2D (HRs 0.43-0.48, p < 0.001). With additional adjustment for plasma glucose, most associations (n = 147 and n = 129 at p < 0.05 and p < 0.01, respectively) remained significant. HRs appeared more extreme among more centrally adipose participants for apolipoprotein B/apolipoprotein A1, BCAA, HDL particle size and docosahexaenoic acid (p for heterogeneity ≤ 0.05). Addition of 31 selected biomarkers to an established T2D risk prediction model modestly, but significantly, improved risk discrimination (c-statistic 0.86 to 0.91, p < 0.001). In relatively lean Chinese adults, diverse metabolic biomarkers are associated with future risk of T2D and can help improve established risk prediction models.

Triglyceride Metabolism Modifies Lipoprotein(a) Plasma Concentration

BACKGROUND

Lipoprotein(a) (Lp(a)) is a significant cardiovascular risk factor. Knowing the mechanisms that regulate its concentration can facilitate the development of Lp(a)-lowering drugs. This study analyzes the relationship between triglycerides (TGs) and Lp(a) concentrations, cross-sectionally and longitudinally, and the influence of the number and composition of TG-rich lipoproteins, and the APOE genotype.

METHODS

Data from Aragon Workers Health Study (AWHS) (n = 5467), National Health and Nutrition Examination Survey III phase 2 (n = 3860), and Hospital Universitario Miguel Servet (HUMS) (n = 2079) were used for cross-sectional TG and Lp(a) relationship. Lp(a) intrasubject variation was studied in AWHS participants and HUMS patients with repeated measurements. TG-rich lipoproteins were quantified by nuclear magnetic resonance in a subsample from AWHS. Apolipoproteins B and E were quantified by Luminex in very low-density lipoprotein (VLDL) isolated by ultracentrifugation, from HUMS samples. APOE genotyping was carried in AWHS and HUMS participants. Regression models adjusted for age and sex were used to study the association.

RESULTS

The 3 studies showed an inverse relationship between TG and Lp(a). Increased VLDL number, size, and TG content were associated with significantly lower Lp(a). There was an inverse association between the apoE concentration in VLDL and Lp(a). No significant association was observed for apolipoprotein (apo)B. Subjects carrying the apoE2/E2 genotype had significantly lower levels of Lp(a).

CONCLUSION

Our results show an inverse relationship Lp(a)-TG. Subjects with larger VLDL size have lower Lp(a), and lower values of Lp(a) were present in patients with apoE-rich VLDL and apoE2/E2 subjects. Our results suggest that bigger VLDLs and VLDLs enriched in apoE are inversely involved in Lp(a) plasma concentration.

1H NMR Metabolite Monitoring during the Differentiation of Human Induced Pluripotent Stem Cells Provides New Insights into the Molecular Events That Regulate Embryonic Chondrogenesis

The integration of cell metabolism with signalling pathways, transcription factor networks and epigenetic mediators is critical in coordinating molecular and cellular events during embryogenesis. Induced pluripotent stem cells (IPSCs) are an established model for embryogenesis, germ layer specification and cell lineage differentiation, advancing the study of human embryonic development and the translation of innovations in drug discovery, disease modelling and cell-based therapies. The metabolic regulation of IPSC pluripotency is mediated by balancing glycolysis and oxidative phosphorylation, but there is a paucity of data regarding the influence of individual metabolite changes during cell lineage differentiation. We used ¹H NMR metabolite fingerprinting and footprinting to monitor metabolite levels as IPSCs are directed in a three-stage protocol through primitive streak/mesendoderm, mesoderm and chondrogenic populations. Metabolite changes were associated with central metabolism, with aerobic glycolysis predominant in IPSC, elevated oxidative phosphorylation during differentiation and fatty acid oxidation and ketone body use in chondrogenic cells. Metabolites were also implicated in the epigenetic regulation of pluripotency, cell signalling and biosynthetic pathways. Our results show that ¹H NMR metabolomics is an effective tool for monitoring metabolite changes during the differentiation of pluripotent cells with implications on optimising media and environmental parameters for the study of embryogenesis and translational applications.

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank

BACKGROUND

Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions.

METHODS

Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model.

RESULTS

Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups.

CONCLUSIONS

Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Circulating metabolites associated with incident myocardial infarction and stroke: A prospective cohort study of 90 438 participants

The relevance between circulating metabolites and vascular events remains controversial and comprehensive studies are lacking. We sought to investigate the prospective associations of plasma metabolomics with risks of incident stroke, ischemic stroke (IS), hemorrhagic stroke (HS), and myocardial infarction (MI). Within the UK Biobank cohort, 249 circulating metabolites were measured in 90 438 participants without baseline vascular diseases. Cox proportional hazards regressions were applied to estimate adjusted hazard ratios (HRs) for per 1 standard deviation increment in metabolites. The least absolute shrinkage and selection operator algorithm was used for selecting metabolite subsets. During a median of 9.0 years of follow-up, we documented 833 incident stroke and 1256 MI cases. Lipid constituents, comprising cholesterol, cholesteryl esters, free cholesterol, phospholipids, and total lipids, in very low- (VLDL), intermediate- (IDL), and low-density lipoprotein (LDL) particles were positively associated with MI risk (HR = 1.12 to 1.36; 95% CI = 1.06 to 1.44), while in high-density lipoprotein (HDL) particles showed inverse associations (HR = 0.68 to 0.81; 95% CI = 0.63 to 0.87). Similar association pattern with MI was also observed for VLDL, IDL, LDL, and HDL particles themselves. In contrast, triglycerides within all lipoproteins, including most HDL particles, were positively associated with MI risk (HR = 1.14 to 1.28; 95% CI = 1.08 to 1.35) and, to a slightly lesser extent, with stroke and IS. Unsaturation of fatty acids and albumin were inversely associated with risks of stroke, IS, and MI. In contrast, the linear association for HS is absent. When combining multiple metabolites, the metabolite risk score captured a drastically elevated risk of all vascular events, about twice that of any single metabolite. Taken together, circulating metabolites showed remarkably widespread associations with incident MI, but substantially weakened associations with risks of stroke and its subtypes. Exhaustive metabolomics profiling may shed light on vascular risk prediction and, in turn, guide pertinent strategies of intervention and treatment.

Prospective Association Between Plasma Amino Acids And Multimorbidity In Older Adults

BACKGROUND

Some amino acids have been associated with aging-related disorders and risk of physical impairment. The aim of this study was to assess the association between plasma concentrations of nine amino acids, including branched-chain and aromatic amino acids, and multimorbidity.

METHODS

This research uses longitudinal data from the Seniors-ENRICA 2 study, a population-based cohort from Spain which comprises non-institutionalized adults older than 65. Blood samples were extracted at baseline and after a follow-up period of two years for a total of 1488 subjects. Participants' information was linked with electronic health records. Chronic diseases were grouped into a list of 60 mutually exclusive conditions. A quantitative measure of multimorbidity, weighting morbidities by their regression coefficients on physical functioning, was employed and ranged from 0 to 100. Generalized estimating equation models were used to explore the relationship between plasma amino acids and multimorbidity, adjusting for sociodemographics, socioeconomic status and lifestyle behaviors.

RESULTS

The mean age of participants at baseline was 73.6 (SD = 4.2) years, 49.6% were women. Higher concentrations of glutamine [coef. per mmol/l (95% confidence interval = 10.1 (3.7, 16.6)], isoleucine [50.3 (21.7, 78.9)] and valine [15.5 (3.1, 28.0)] were significantly associated with higher multimorbidity scores, after adjusting for potential confounders. Body mass index could have influenced the relationship between isoleucine and multimorbidity (p = 0.016).

CONCLUSIONS

Amino acids could play a role in regulating aging-related diseases. Glutamine and branched-chain amino acids as isoleucine and valine are prospectively associated and could serve as risk markers for multimorbidity in older adults.

Metabolic changes associated with two endocrine abnormalities in dogs: elevated fructosamine and low thyroxine

INTRODUCTION

Metabolomics studies in canine endocrine abnormalities are sparse and basic information on these abnormalities must be generated.

OBJECTIVES

To characterize the metabolic changes associated with elevated fructosamine, reflecting poor glycemic control, and low thyroxine, a thyroid hormone controlling metabolism.

METHODS

Leftovers of clinical serum samples; 25 controls, 79 high fructosamine, and 47 low thyroxine, were analyzed using ¹H NMR and differences were evaluated using Firth logistic regression.

RESULTS

Both high fructosamine and low thyroxine were associated with changes in concentrations of multiple metabolites, including glycoprotein acetyls and lipids.

CONCLUSION

These findings suggest promising makers for further research and clinical validation.

Visceral adiposity is associated with metabolic profiles predictive of type 2 diabetes and myocardial infarction

Background

Visceral fat (VF) increases risk for cardiometabolic disease (CMD), the leading cause of morbidity and mortality. Variations in the circulating metabolome predict the risk for CMD but whether or not this is related to VF is unknown. Further, CMD is now also present in adolescents, and the relationships between VF, circulating metabolome, and CMD may vary between adolescents and adults.

Methods

With an aim to add understanding to the metabolic variations in visceral obesity, we tested associations between VF, measured directly with magnetic resonance imaging, and 228 fasting serum metabolomic measures, quantified with nuclear magnetic resonance spectroscopy, in 507 adults (36-65 years) and 938 adolescents (12-18 years). We further utilized data from published studies to estimate similarities between VF and CMD-associated metabolic profiles.

Results

Here we show that VF, independently of body mass index (BMI) or subcutaneous fat, is associated with triglyceride-rich lipoproteins, fatty acids, and inflammation in both adults and adolescents, whereas the associations with amino acids, glucose, and intermediary metabolites are significant in adults only. BMI-adjusted metabolomic profile of VF resembles those predicting type 2 diabetes in adults (R 2 = 0.88) and adolescents (R 2 = 0.70), and myocardial infarction in adults (R 2 = 0.59) and adolescents (R 2 = 0.40); this is not the case for ischemic stroke (adults: R 2 = 0.05, adolescents: R 2 = 0.08).

Conclusions

Visceral adiposity is associated with metabolomic profiles predictive of type 2 diabetes and myocardial infarction even in normal-weight individuals and already in adolescence. Targeting factors contributing to the emergence and maintenance of these profiles might ameliorate their cumulative effects on cardiometabolic health.

Genetic and observational evidence: No independent role for cholesterol efflux over static high-density lipoprotein concentration measures in coronary heart disease risk assessment

BACKGROUND

Observational findings for high-density lipoprotein (HDL)-mediated cholesterol efflux capacity (HDL-CEC) and coronary heart disease (CHD) appear inconsistent, and knowledge of the genetic architecture of HDL-CEC is limited.

OBJECTIVES

A large-scale observational study on the associations of HDL-CEC and other HDL-related measures with CHD and the largest genome-wide association study (GWAS) of HDL-CEC.

PARTICIPANTS/METHODS

Six independent cohorts were included with follow-up data for 14,438 participants to investigate the associations of HDL-related measures with incident CHD (1,570 events). The GWAS of HDL-CEC was carried out in 20,372 participants.

RESULTS

HDL-CEC did not associate with CHD when adjusted for traditional risk factors and HDL cholesterol (HDL-C). In contradiction, almost all HDL-related concentration measures associated consistently with CHD after corresponding adjustments. There were no genetic loci associated with HDL-CEC independent of HDL-C and triglycerides.

CONCLUSION

HDL-CEC is not unequivocally associated with CHD in contrast to HDL-C, apolipoprotein A-I, and most of the HDL subclass particle concentrations.

Circulating Polyunsaturated Fatty Acids and COVID-19: A Prospective Cohort Study and Mendelian Randomization Analysis

Higher circulating polyunsaturated fatty acids (PUFAs), especially omega-3 fatty acids, have been linked to a better prognosis in patients of coronavirus disease 2019 (COVID-19). However, the effects and causality of pre-infection PUFA levels remain unclear. This study aimed to investigate the observational and causal associations of circulating PUFAs with COVID-19 susceptibility and severity. We first performed a prospective cohort study in UK Biobank, with 20,626 controls who were tested negative and 4,101 COVID-19 patients, including 970 hospitalized ones. Plasma PUFAs at baseline (blood samples collected from 2007 to 2010) were measured by nuclear magnetic resonance, including total PUFAs, omega-3 PUFAs, omega-6 PUFAs, docosahexaenoic acid (DHA), linoleic acid (LA), and the omega-6/omega-3 ratio. Moreover, going beyond UK Biobank, we leveraged summary statistics from existing genome-wide association studies to perform bidirectional two-sample Mendelian randomization (MR) analyses to examine the causal associations of eight individual PUFAs, measured in either plasma or red blood cells, with COVID-19 susceptibility and severity. In the observational association analysis of each PUFA measure separately, total, omega-3, and omega-6 PUFAs, DHA, and LA were associated with a lower risk of severe COVID-19. Omega-3 PUFAs and DHA were also associated with a lower risk of testing positive for COVID-19. The omega-6/omega-3 ratio was positively associated with risks of both susceptibility and severity. When omega-6, omega-3, and their ratio are jointly analyzed, only omega-3 PUFAs remained significantly and inversely associated with both susceptibility and severity. The forward MR analysis indicated that docosapentaenoic acid (DPA-n3) and arachidonic acid (AA) might be causally associated with a lower risk of severe COVID-19, with OR (95% CI) per one SD increase in the plasma level as 0.89 (0.81, 0.99) and 0.96 (0.94, 0.99), respectively. The reverse MR analysis did not support any causal effect of COVID-19 on PUFAs. Our observational analysis supported that higher circulating omega-3 PUFAs, especially DHA, may lower the susceptibility to and alleviate the severity of COVID-19. Our MR analysis further supported causal associations of DPA-n3 and AA with a lower risk of severe COVID-19.

Apt interpretation of comprehensive lipoprotein data in large-scale epidemiology: disclosure of fundamental structural and metabolic relationships

BACKGROUND

Quantitative lipoprotein analytics using nuclear magnetic resonance (NMR) spectroscopy is currently commonplace in large-scale studies. One methodology has become widespread and is currently being utilized also in large biobanks. It allows the comprehensive characterization of 14 lipoprotein subclasses, clinical lipids, apolipoprotein A-I and B. The details of these data are conceptualized here in relation to lipoprotein metabolism with particular attention on the fundamental characteristics of subclass particle numbers, lipid concentrations and compositional measures.

METHODS AND RESULTS

The NMR methodology was applied to fasting serum samples from Northern Finland Birth Cohorts 1966 and 1986 with 5651 and 5605 participants, respectively. All results were highly consistent between the cohorts. Circulating lipid concentrations in a particular lipoprotein subclass arise predominantly as the result of the circulating number of those subclass particles. The spherical lipoprotein particle shape, with a radially oriented surface monolayer, imposes size-dependent biophysical constraints for the lipid composition of individual subclass particles and inherently restricts the accommodation of metabolic changes via compositional modifications. The new finding that the relationship between lipoprotein subclass particle concentrations and the particle size is log-linear reveals that circulating lipoprotein particles are also under rather strict metabolic constraints for both their absolute and relative concentrations.

CONCLUSIONS

The fundamental structural and metabolic relationships between lipoprotein subclasses elucidated in this study empower detailed interpretation of lipoprotein metabolism. Understanding the intricate details of these extensive data is important for the precise interpretation of novel therapeutic opportunities and for fully utilizing the potential of forthcoming analyses of genetic and metabolic data in large biobanks.

Role of circulating polyunsaturated fatty acids on cardiovascular diseases risk: analysis using Mendelian randomization and fatty acid genetic association data from over 114,000 UK Biobank participants

BACKGROUND

Despite early interest in the health effects of polyunsaturated fatty acids (PUFA), there is still substantial controversy and uncertainty on the evidence linking PUFA to cardiovascular diseases (CVDs). We investigated the effect of plasma concentration of omega-3 PUFA (i.e. docosahexaenoic acid (DHA) and total omega-3 PUFA) and omega-6 PUFA (i.e. linoleic acid and total omega-6 PUFA) on the risk of CVDs using Mendelian randomization.

METHODS

We conducted the largest genome-wide association study (GWAS) of circulating PUFA to date including a sample of 114,999 individuals and incorporated these data in a two-sample Mendelian randomization framework to investigate the involvement of circulating PUFA on a wide range of CVDs in up to 1,153,768 individuals of European ancestry (i.e. coronary artery disease, ischemic stroke, haemorrhagic stroke, heart failure, atrial fibrillation, peripheral arterial disease, aortic aneurysm, venous thromboembolism and aortic valve stenosis).

RESULTS

GWAS identified between 46 and 64 SNPs for the four PUFA traits, explaining 4.8-7.9% of circulating PUFA variance and with mean F statistics >100. Higher genetically predicted DHA (and total omega-3 fatty acids) concentration was related to higher risk of some cardiovascular endpoints; however, these findings did not pass our criteria for multiple testing correction and were attenuated when accounting for LDL-cholesterol through multivariable Mendelian randomization or excluding SNPs in the vicinity of the FADS locus. Estimates for the relation between higher genetically predicted linoleic acid (and total omega-6) concentration were inconsistent across different cardiovascular endpoints and Mendelian randomization methods. There was weak evidence of higher genetically predicted linoleic acid being related to lower risk of ischemic stroke and peripheral artery disease when accounting by LDL-cholesterol.

CONCLUSIONS

We have conducted the largest GWAS of circulating PUFA to date and the most comprehensive Mendelian randomization analyses. Overall, our Mendelian randomization findings do not support a protective role of circulating PUFA concentration on the risk of CVDs. However, horizontal pleiotropy via lipoprotein-related traits could be a key source of bias in our analyses.

High density lipoprotein subfractions and extent of coronary atherosclerotic lesions: From the cordioprev study

BACKGROUND AND AIMS

The extent of atherosclerotic coronary heart disease (CHD) is associated with its prognosis, thus discovering potential biomarkers related to worse outcomes could prove valuable. The present work aims to investigate whether lipoprotein subfractions are associated with angiographic CHD severity.

MATERIALS AND METHODS

Patients from the CORDIOPREV study exhibiting coronary lesions in angiography were classified into two groups (single-vessel coronary disease (SVD) or multivessel coronary disease (MVD)). High-throughput nuclear magnetic resonance (NMR) spectroscopy determined lipoprotein subfractions concentration and composition.

RESULTS

SVD patients showed a higher concentration of medium and small HDL particles compared with MVD patients. For medium HDL, total lipids, phospholipids, total cholesterol, cholesteryl esters and free cholesterol reflected HDL particle concentration, whereas, for small HDL, total lipids, phospholipids, and free cholesterol mirrored lipoprotein particle concentration. Among traditional cardiovascular risk factors, age, hypertension and T2D were independently associated with angiography severity. In multivariate logistic regression models, medium and small HDL particles remained inversely associated with angiography severity (OR 0.77 (95% CI: 0.64-0.91); OR 0.78 (95% CI: 0.67-0.91), respectively) after adjusting with covariates.

CONCLUSION

In CHD patients mostly on statin treatment, angiography severity is inversely related to small and medium HDL subclasses concentration measured by NMR. These particles are also independent predictors of the presence of MVD, and its use increased the prediction of this entity over traditional risk factors.

Metabolomics profile of 5649 users and non-users of hormonal intrauterine devices in Finland

BACKGROUND

Use of hormonal intrauterine devices has grown during the last decades. Although the hormonal intrauterine devices act mostly via local effects on uterus, measurable concentrations of levonorgestrel are absorbed into the systemic circulation. The possible metabolic changes and large scale biomarker profiles associated with the hormonal intrauterine devices have not yet been studied in detail.

OBJECTIVES

To examine, through the metabolomics approach, the metabolic profile of the hormonal intrauterine device use, its associations as a function of the duration of use, as well as those with after discontinuation of the hormonal intrauterine device use.

STUDY DESIGN

The study consists of cross-sectional analyses of five population-based surveys (FINRISK and FinHealth studies), spanning 1997-2017. All fertile aged (18-49 years) participants in the surveys with available information on hormonal contraceptive use and metabolomics data (n=5649), were included in the study. Altogether 211 metabolic measures in users of hormonal intrauterine devices (n=1006) were compared to those in non-users of hormonal contraception (n=4643) via multivariable linear regression models. In order to allow the comparison across multiple measures, association magnitudes are reported in SD units of difference in biomarker concentration compared to the reference group.

RESULTS

After adjustment for covariates, levels of 141 metabolites differed in current users of hormonal intrauterine devices compared to non-users of hormonal contraception (median difference in biomarker concentration: 0.09 SD): lower levels of particle concentration of larger lipoprotein subclasses, triglycerides, cholesterol and derivatives, apolipoproteins A and B, fatty acids, glycoprotein acetyls and aromatic amino acids. The metabolic pattern of the hormonal intrauterine device use did not change according to the duration of use. When comparing previous users and never-users of hormonal intrauterine devices, no significant metabolic differences emerged.

CONCLUSIONS

The use of hormonal intrauterine devices was associated with several moderate metabolic changes, previously associated with reduced arterial cardiometabolic risk. The metabolic effects were independent of the duration of use of the hormonal intrauterine devices. Moreover, the metabolic profiles were similar after discontinuation of the hormonal intrauterine devices and in never-users.

Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight

Marked physiological changes in pregnancy are essential to support foetal growth; however, evidence on the role of specific maternal metabolic traits from human studies is limited. We integrated Mendelian randomisation (MR) and metabolomics data to probe the effect of 46 maternal metabolic traits on offspring birthweight (N = 210,267). We implemented univariable two-sample MR (UVMR) to identify candidate metabolic traits affecting offspring birthweight. We then applied two-sample multivariable MR (MVMR) to jointly estimate the potential direct causal effect for each candidate maternal metabolic trait. In the main analyses, UVMR indicated that higher maternal glucose was related to higher offspring birthweight (0.328 SD difference in mean birthweight per 1 SD difference in glucose (95% CI: 0.104, 0.414)), as were maternal glutamine (0.089 (95% CI: 0.033, 0.144)) and alanine (0.137 (95% CI: 0.036, 0.239)). In additional analyses, UVMR estimates were broadly consistent when selecting instruments from an independent data source, albeit imprecise for glutamine and alanine, and were attenuated for alanine when using other UVMR methods. MVMR results supported independent effects of these metabolites, with effect estimates consistent with those seen with the UVMR results. Among the remaining 43 metabolic traits, UVMR estimates indicated a null effect for most lipid-related traits and a high degree of uncertainty for other amino acids and ketone bodies. Our findings suggest that maternal gestational glucose and glutamine are causally related to offspring birthweight.

Aminoaciduria and metabolic dysregulation during diabetic ketoacidosis: Results from the diabetic kidney alarm (DKA) study

OBJECTIVE

We examined changes in the excretion of various amino acids and in glycolysis and ketogenesis-related metabolites, during and after diabetic ketoacidosis (DKA) diagnosis, in youth with known or new onset type 1 diabetes (T1D).

METHODS

Urine samples were collected from 40 youth with DKA (52% boys, mean age 11 ± 4 years, venous pH 7.2 ± 0.1, blood glucose 451 ± 163 mg/dL) at 3 time points: 0-8 h and 12-24 h after starting an insulin infusion, and 3 months after hospital discharge. Mixed-effects models evaluated the changes in amino acids and other metabolites in the urine.

RESULTS

Concentrations of urine histidine, threonine, tryptophan, and leucine per creatinine were highest at 0-8 h (148.8 ± 23.5, 59.5 ± 12.3, 15.4 ± 1.4, and 24.5 ± 2.4% of urine creatinine, respectively), and significantly decreased over 3 months (p = 0.028, p = 0.027, p = 0.019, and p < 0.0001, respectively). Urine histidine, threonine, tryptophan, and leucine per urine creatinine decreased by 10.6 ± 19.2, 0.7 ± 0.9, 1.3 ± 0.9, and 0.5 ± 0.3-fold, respectively, between 0 and 8 h and 3 months.

CONCLUSIONS

In our study, DKA was associated with profound aminoaciduria, suggestive of proximal tubular dysfunction analogous to Fanconi syndrome.

Metabolomic Profiling of Samples from Pediatric Asthma Patients Unveils Deficient Nutrients in African Americans

Plasma metabolomics represents a potentially powerful approach to understand the biochemical mechanisms of nutrition and metabolism in asthma. This study aims to acquire knowledge on plasma metabolites in asthma, which may provide avenues for nutrition therapy, as well as explanations for the observed effects in existing therapies. This study investigated 249 metabolites from 18 metabolite groups in a large cohort of African American population, including 602 pediatric patients with asthma and 593 controls, using a nuclear magnetic resonance (NMR) metabolomics platform. Decreased levels of citrate, ketone bodies, and two amino acids histidine (His) and glutamine (Gln), were observed in asthma cases compared to controls. Metabolites for lipid metabolism lost significance after controlling for comorbid obesity. For the first time, this study depicts a broad panorama of lipid metabolism and nutrition in asthma. Supplementation or augmentation of nutrients that are deficient may be beneficial for asthma care.

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Asthma is a major health issue in African Americans

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Metabolomics represents a powerful approach to understand the metabolism in asthma

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We observed decreased citrate, ketone bodies, and amino acids in the plasma

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Supplementation of nutrients that are deficient may be beneficial for asthma care

The newborn metabolome: associations with gestational diabetes, sex, gestation, birth mode, and birth weight

BACKGROUND

Pathways towards many adult-onset conditions begin early in life, even in utero. Maternal health in pregnancy influences this process, but little is known how it affects neonatal metabolism. We investigated associations between pregnancy and birth factors and cord blood metabolomic profile in a large, population-derived cohort.

METHODS

Metabolites were measured using nuclear magnetic resonance in maternal (28 weeks gestation) and cord serum from 912 mother-child pairs in the Barwon Infant Study pre-birth cohort. Associations between maternal (metabolites, age, BMI, smoking), pregnancy (pre-eclampsia, gestational diabetes (GDM)), and birth characteristics (delivery mode, gestational age, weight, infant sex) with 72 cord blood metabolites were examined by linear regression.

RESULTS

Delivery mode, sex, gestational age, and birth weight were associated with specific metabolite levels in cord blood, including amino acids, fatty acids, and cholesterols. GDM was associated with higher cord blood levels of acetoacetate and 3-hydroxybutyrate.

CONCLUSIONS

Neonatal factors, particularly delivery mode, were associated with many cord blood metabolite differences, including those implicated in later risk of cardiometabolic disease. Associations between GDM and higher offspring ketone levels at birth are consistent with maternal ketosis in diabetic pregnancies. Further work is needed to determine whether these neonatal metabolome differences associate with later health outcomes.

IMPACT

Variations in blood metabolomic profile have been linked to health status in adults and children, but corresponding data in neonates are scarce. We report evidence that pregnancy complications, mode of delivery, and offspring characteristics, including sex, are independently associated with a range of circulating metabolites at birth, including ketone bodies, amino acids, cholesterols, and inflammatory markers. Independent of birth weight, exposure to gestational diabetes is associated with higher cord blood ketone bodies and citrate. These findings suggest that pregnancy complications, mode of delivery, gestational age, and measures of growth influence metabolic pathways prior to birth, potentially impacting later health and development.

Association of egg consumption, metabolic markers, and risk of cardiovascular diseases: A nested case-control study

Background

Few studies have assessed the role of individual plasma cholesterol levels in the association between egg consumption and the risk of cardiovascular diseases. This research aims to simultaneously explore the associations of self-reported egg consumption with plasma metabolic markers and these markers with the risk of cardiovascular disease (CVD).

Methods

Totally 4778 participants (3401 CVD cases subdivided into subtypes and 1377 controls) aged 30-79 were selected based on the China Kadoorie Biobank. Targeted nuclear magnetic resonance was used to quantify 225 metabolites in baseline plasma samples. Linear regression was conducted to assess associations between self-reported egg consumption and metabolic markers, which were further compared with associations between metabolic markers and CVD risk.

Results

Egg consumption was associated with 24 out of 225 markers, including positive associations for apolipoprotein A1, acetate, mean HDL diameter, and lipid profiles of very large and large HDL, and inverse associations for total cholesterol and cholesterol esters in small VLDL. Among these 24 markers, 14 were associated with CVD risk. In general, the associations of egg consumption with metabolic markers and of these markers with CVD risk showed opposite patterns.

Conclusions

In the Chinese population, egg consumption is associated with several metabolic markers, which may partially explain the protective effect of moderate egg consumption on CVD.

Funding

This work was supported by the National Natural Science Foundation of China (81973125, 81941018, 91846303, 91843302). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants (2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 81390541, 81390544), and Chinese Ministry of Science and Technology (2011BAI09B01). The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication.

Early life infection and proinflammatory, atherogenic metabolomic and lipidomic profiles in infancy: a population-based cohort study

Background:

The risk of adult onset cardiovascular and metabolic (cardiometabolic) disease accrues from early life. Infection is ubiquitous in infancy and induces inflammation, a key cardiometabolic risk factor, but the relationship between infection, inflammation, and metabolic profiles in early childhood remains unexplored. We investigated relationships between infection and plasma metabolomic and lipidomic profiles at age 6 and 12 months, and mediation of these associations by inflammation.

Methods:

Matched infection, metabolomics, and lipidomics data were generated from 555 infants in a pre-birth longitudinal cohort. Infection data from birth to 12 months were parent-reported (total infections at age 1, 3, 6, 9, and 12 months), inflammation markers (high-sensitivity C-reactive protein [hsCRP]; glycoprotein acetyls [GlycA]) were quantified at 12 months. Metabolic profiles were 12-month plasma nuclear magnetic resonance metabolomics (228 metabolites) and liquid chromatography/mass spectrometry lipidomics (776 lipids). Associations were evaluated with multivariable linear regression models. In secondary analyses, corresponding inflammation and metabolic data from birth (serum) and 6-month (plasma) time points were used.

Results:

At 12 months, more frequent infant infections were associated with adverse metabolomic (elevated inflammation markers, triglycerides and phenylalanine, and lower high-density lipoprotein [HDL] cholesterol and apolipoprotein A1) and lipidomic profiles (elevated phosphatidylethanolamines and lower trihexosylceramides, dehydrocholesteryl esters, and plasmalogens). Similar, more marked, profiles were observed with higher GlycA, but not hsCRP. GlycA mediated a substantial proportion of the relationship between infection and metabolome/lipidome, with hsCRP generally mediating a lower proportion. Analogous relationships were observed between infection and 6-month inflammation, HDL cholesterol, and apolipoprotein A1.

Conclusions:

Infants with a greater infection burden in the first year of life had proinflammatory and proatherogenic plasma metabolomic/lipidomic profiles at 12 months of age that in adults are indicative of heightened risk of cardiovascular disease, obesity, and type 2 diabetes. These findings suggest potentially modifiable pathways linking early life infection and inflammation with subsequent cardiometabolic risk.

Funding:

The establishment work and infrastructure for the BIS was provided by the Murdoch Children’s Research Institute (MCRI), Deakin University, and Barwon Health. Subsequent funding was secured from National Health and Medical Research Council of Australia (NHMRC), The Shepherd Foundation, The Jack Brockhoff Foundation, the Scobie & Claire McKinnon Trust, the Shane O’Brien Memorial Asthma Foundation, the Our Women’s Our Children’s Fund Raising Committee Barwon Health, the Rotary Club of Geelong, the Minderoo Foundation, the Ilhan Food Allergy Foundation, GMHBA, Vanguard Investments Australia Ltd, and the Percy Baxter Charitable Trust, Perpetual Trustees. In-kind support was provided by the Cotton On Foundation and CreativeForce. The study sponsors were not involved in the collection, analysis, and interpretation of data; writing of the report; or the decision to submit the report for publication. Research at MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program. This work was also supported by NHMRC Senior Research Fellowships to ALP (1008396); DB (1064629); and RS (1045161) , NHMRC Investigator Grants to ALP (1110200) and DB (1175744), NHMRC-A*STAR project grant (1149047). TM is supported by an MCRI ECR Fellowship. SB is supported by the Dutch Research Council (452173113).

Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study

BACKGROUND

Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors.

METHODS

Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.

RESULTS

During median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]) and relative (15.0% [10.5-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).

CONCLUSIONS

When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.

Serum metabolomic profiles associated with subclinical and clinical cardiovascular phenotypes in people with type 2 diabetes

BACKGROUND

Atherosclerotic cardiovascular diseases (CVD) is the leading cause of death in diabetes, but the full range of biomarkers reflecting atherosclerotic burden and CVD risk in people with diabetes is unknown. Metabolomics may help identify novel biomarkers potentially involved in development of atherosclerosis. We investigated the serum metabolomic profile of subclinical atherosclerosis, measured using ankle brachial index (ABI), in people with type 2 diabetes, compared with the profile for symptomatic CVD in the same population.

METHODS

The Edinburgh Type 2 Diabetes Study is a cohort of 1,066 individuals with type 2 diabetes. ABI was measured at baseline, years 4 and 10, with cardiovascular events assessed at baseline and during 10 years of follow-up. A panel of 228 metabolites was measured at baseline using nuclear magnetic resonance spectrometry, and their association with both ABI and prevalent CVD was explored using univariate regression models and least absolute shrinkage and selection operator (LASSO). Metabolites associated with baseline ABI were further explored for association with follow-up ABI and incident CVD.

RESULTS

Mean (standard deviation, SD) ABI at baseline was 0.97 (0.18, N = 1025), and prevalence of CVD was 35.0%. During 10-year follow-up, mean (SD) change in ABI was + 0.006 (0.178, n = 436), and 257 CVD events occurred. Lactate, glycerol, creatinine and glycoprotein acetyls levels were associated with baseline ABI in both univariate regression [βs (95% confidence interval, CI) ranged from - 0.025 (- 0.036, - 0.015) to - 0.023 (- 0.034, - 0.013), all p < 0.0002] and LASSO analysis. The associations remained nominally significant after adjustment for major vascular risk factors. In prospective analyses, lactate was nominally associated with ABI measured at years 4 and 10 after adjustment for baseline ABI. The four ABI-associated metabolites were all positively associated with prevalent CVD [odds ratios (ORs) ranged from 1.29 (1.13, 1.47) to 1.49 (1.29, 1.74), all p < 0.0002], and they were also positively associated with incident CVD [ORs (95% CI) ranged from 1.19 (1.02, 1.39) to 1.35 (1.17, 1.56), all p < 0.05].

CONCLUSIONS

Serum metabolites relating to glycolysis, fluid balance and inflammation were independently associated with both a marker of subclinical atherosclerosis and with symptomatic CVD in people with type 2 diabetes. Additional investigation is warranted to determine their roles as possible etiological and/or predictive biomarkers for atherosclerotic CVD.

HDL particle subclasses in statin treated patients with peripheral artery disease predict long-term survival

Low-density lipoprotein-cholesterol (LDL-C) reduction showed a strong reduction of cardiovascular (CV) event rates in CV disease. However, the residual risk of future CV events remains high, which especially extends to peripheral arterial disease (PAD). Nuclear magnetic resonance (NMR)-spectroscopy offers a novel method for analyses of the lipoprotein spectrum. This study investigates lipoprotein subclasses using NMR-spectroscopy and assesses implications for long-term survival in PAD. NMR-spectroscopy was performed by Nightingale Inc. in 319 patients with stable PAD and well-controlled CV risk factors. Patients were followed-up for ten years. During that period 123 patients (38.5%) died, of those 68 (21.3%) were defined as CV-deaths. Outcome data were analyzed by the Kaplan-Meier method and multivariable Cox regression for lipoprotein particles. Small and medium high-density lipoprotein-particles (S-HDL-P and M-HDL-P) showed a significant inverse association with all-cause mortality in Cox-regression analyses after multivariable adjustment (S-HDL-P hazard ratio 0.71, 95% confidence interval 0.57-0.88; M-HDL-P 0.72, 0.58-0.90) for each increase of 1 standard deviation. In contrast, cholesterol-rich x-large HDL-particles (XL-HDL-P) showed a positive association with all-cause mortality (1.51, 1.20-1.89). Only the association between XL-HDL-P and CV-death sustained multivariable adjustment (1.49, 1.10-2.02), whereas associations for S-HDL-P and M-HDL-P were attenuated (0.76, 0.57-1.01; 0.80, 0.60-1.06). This study shows a novel association for a beneficial role of S-HDL-P and M-HDL-P but a negative association with higher cholesterol-rich XL-HDL-P for long-term outcome in well-treated patients with PAD. Thus, these results provide evidence that NMR-measured HDL particles identify patients at high CV residual risk beyond adequate lipid-lowering therapy.

Circulating Metabolome and White Matter Hyperintensities in Women and Men

BACKGROUND

White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites.

METHODS

We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)<0.05 were considered significant.

RESULTS

In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10-7) and in both the pooled sample (PFDR.full.adj=1.66×10-4) and statin-untreated (PFDR.full.adj=1.65×10-6) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (PFDR=0.047).

CONCLUSIONS

Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Metabolomic serum abnormalities in dogs with hepatopathies

Hepatopathies can cause major metabolic abnormalities in humans and animals. This study examined differences in serum metabolomic parameters and patterns in left-over serum samples from dogs with either congenital portosystemic shunts (cPSS, n = 24) or high serum liver enzyme activities (HLEA, n = 25) compared to control dogs (n = 64). A validated targeted proton nuclear magnetic resonance spectroscopy platform was used to assess 123 parameters. Principal component analysis of the serum metabolome demonstrated distinct clustering among individuals in each group, with the cluster of HLEA being broader compared to the other groups, presumably due to the wider spectrum of hepatic diseases represented in these samples. While younger and older adult control dogs had very similar metabolomic patterns and clusters, there were changes in many metabolites in the hepatopathy groups. Higher phenylalanine and tyrosine concentrations, lower branched-chained amino acids (BCAAs) concentrations, and altered fatty acid parameters were seen in cPSS dogs compared to controls. In contrast, dogs with HLEA had increased concentrations of BCAAs, phenylalanine, and various lipoproteins. Machine learning based solely on the metabolomics data showed excellent group classification, potentially identifying a novel tool to differentiate hepatopathies. The observed changes in metabolic parameters could provide invaluable insight into the pathophysiology, diagnosis, and prognosis of hepatopathies.

The metabolic signature of cardiovascular disease and arterial calcification in patients with chronic kidney disease

BACKGROUND AND AIMS

The relationship between chronic kidney disease (CKD) and cardiovascular events is well-established. Clinically recognised risk factors of cardiovascular disease cannot fully explain this association. The objective of the present cross-sectional study was to investigate associations between serum metabolites and prevalent cardiovascular disease, as well as subclinical cardiovascular disease measured as coronary artery calcium score (CACS) in patients with CKD.

METHODS

More than 200 preselected metabolites were quantified using nuclear magnetic resonance spectroscopy in 725 patients and 174 controls from the Copenhagen CKD Cohort. CACS was determined by computed tomography.

RESULTS

Mean age of patients was 57.8 years, and 444 (61.3%) were men. Most of patients had hypercholesterolemia, and 133 (18.3%) had type 2 diabetes. Overall, 85 metabolites were significantly associated with prevalent cardiovascular disease in a model adjusted for eGFR, age, and sex, as well as Bonferroni correction for multiple testing (p < 0.001). After further adjusting for diabetes, BMI, smoking, and cholesterol-lowering medication, the significance was lost for all but six metabolites (concentration of ApoA-1, cholesterol in total HDL and HDL2, total lipids and phospholipids in large HDL particles, and the ratio of phospholipids to total lipids in smaller VLDL particles). Of the 85 metabolites associated with prevalent cardiovascular disease, 71 were also associated with CACS in a similar pattern. Yet, in the model adjusted for all seven cardiovascular risk factors, only serum glucose levels and the ratio of triglycerides to total lipids in larger LDL particles remained significant.

CONCLUSIONS

In patients with CKD, associations with prevalent cardiovascular disease were mainly found for HDL-related metabolites, while CACS was associated with glucose levels and increased triglycerides to total lipids ratio in LDL particles.

Circulating polyunsaturated fatty acids and COVID-19: a prospective cohort study and Mendelian randomization analysis

BACKGROUND

Higher circulating polyunsaturated fatty acids (PUFAs), especially omega-3 ones, have been linked to a better prognosis in patients of coronavirus disease 2019 (COVID-19). However, the effects and causality of pre-infection PUFA levels remain unclear.

OBJECTIVE

To investigate the observational and causal associations of circulating PUFAs with COVID-19 susceptibility and severity.

DESIGN

We first performed a prospective cohort study in UK Biobank, with 20,626 controls who were tested negative and 4,101 COVID-19 patients, including 970 hospitalized ones. Plasma PUFAs at baseline were measured by nuclear magnetic resonance, including total PUFAs, omega-3 PUFAs, omega-6 PUFAs, docosahexaenoic acid (DHA), linoleic acid (LA), and the omega-6/omega-3 ratio. Moreover, bidirectional two-sample Mendelian randomization (MR) analyses were performed to examine the causal associations of eight individual PUFAs, measured in either plasma or red blood cells, with COVID-19 susceptibility and severity using summary statistics from existing genome-wide association studies.

RESULTS

In the observational association analysis, total PUFAs, omega-3 PUFAs, omega-6 PUFAs, DHA, and LA were associated with a lower risk of severe COVID-19. Omega-3 PUFAs and DHA were also associated with a lower risk of testing positive for COVID-19. The omega-6/omega-3 ratio was positively associated with risks of both susceptibility and severity. The forward MR analysis indicated that arachidonic acid (AA) and docosapentaenoic acid (DPA-n3) might be causally associated with a lower risk of severe COVID-19, with OR (95% CI) per one SD increase in the plasma level as 0.96 (0.94, 0.99) and 0.89 (0.81, 0.99), respectively. The reverse MR analysis did not support any causal effect of COVID-19 on PUFAs.

CONCLUSIONS

Our observational analysis supported that higher circulating PUFAs, either omega-3 or omega-6, are protective against severe COVID-19, while omega-3 PUFAs, especially DHA, were also associated with reducing COVID-19 susceptibility. Our MR analysis further supported causal associations of AA and DPA-n3 with a lower risk of severe COVID-19.

Circulating Metabolites and the Development of Type 2 Diabetes in Chinese Adults

OBJECTIVE

To assess prospective associations of circulating metabolites with the risk of type 2 diabetes (T2D) among Chinese adults.

RESEARCH DESIGN AND METHODS

A case-cohort study within the 8-year prospective China Kadoorie Biobank comprised 882 participants with incident T2D and 789 subcohort participants. Nuclear magnetic resonance metabolomic profiling quantified 225 metabolites in stored baseline plasma samples. Cox regression related individual metabolites with T2D risk, adjusting for potential confounders and fasting time.

RESULTS

After correction for multiple testing, 163 metabolites were significantly associated with the risk of T2D (P < 0.05). There were strong positive associations of VLDL particle size, the ratio of apolipoprotein B to apolipoprotein A-1, branched-chain amino acids, glucose, and triglycerides with T2D, and inverse associations of HDL-cholesterol, HDL particle size, and relative n-3 and saturated fatty acid concentrations.

CONCLUSIONS

In Chinese adults, metabolites across diverse pathways were independently associated with T2D risk, providing valuable etiological insights and potential to improve T2D risk prediction.

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.

Multivariate analysis of NMR-based metabolomic data

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Cohort profile: InTraUterine sampling in early pregnancy (ITU), a prospective pregnancy cohort study in Finland: study design and baseline characteristics

PURPOSE

The InTraUterine sampling in early pregnancy (ITU) is a prospective pregnancy cohort study. The overarching aim of ITU is to unravel genomic, epigenomic, transcriptomic, endocrine, inflammatory and metabolic maternal-placental-fetal mechanisms involved in the programming of health and disease after exposure to prenatal environmental adversity, such as maternal malnutrition, cardiometabolic disorders, infections, medical interventions, mental disorders and psychosocial stress. This paper describes the study protocol, design and baseline characteristics of the cohort.

PARTICIPANTS

We included 944 pregnant Finnish women, their partners and children born alive between April 2012 and December 2017. The women were recruited through the national, voluntary trisomy 21 screening between 9⁺⁰ and 21⁺⁶ gestational weeks. Of the participating women, 543 were screen positive and underwent fetal chromosomal testing. Test result of these women suggested no fetal chromosomal abnormality. Further, we recruited 401 women who were screen negative and who did not undergo fetal chromosomal testing.

FINDINGS TO DATE

We have collected chorionic villi and amniotic fluid from the screen-positive women; blood, urine, buccal swabs and diurnal salivary samples from all women; blood and buccal swabs from all partners; and placenta, cord blood and buccal swabs from all newborns for analyses of the genome, epigenome, transcriptome, and endocrine, inflammatory and metabolic markers. These data are coupled with comprehensive phenotypes, including questions on demographic characteristics, health and well-being of the women and their partners during pregnancy and of the women and their children at the child's age of 1.7 and 3 years. Data also come from patient records and nationwide registers covering health, lifestyle and medication data.

FUTURE PLANS

Multiple layers of ITU data allow integrative data analyses, which translate to biomarker identification and allow risk stratification and understanding of the biological mechanisms involved in prenatal programming of health and disease.

Diverging metabolic effects of 2 energy-restricted diets differing in nutrient quality: a 12-week randomized controlled trial in subjects with abdominal obesity

BACKGROUND

Despite the established relation between energy restriction (ER) and metabolic health, the most beneficial nutrient composition of a weight-loss diet is still a subject of debate.

OBJECTIVES

The aim of the study was to examine the additional effects of nutrient quality on top of ER.

METHODS

A parallel-designed, 12-week 25% ER dietary intervention study was conducted (clinicaltrials.gov: NCT02194504). Participants aged 40-70 years with abdominal obesity were randomized over 3 groups: a 25% ER high-nutrient-quality diet (n = 40); a 25% ER low-nutrient-quality diet (n = 40); or a habitual diet (n = 30). Both ER diets were nutritionally adequate, and the high-nutrient-quality ER diet was enriched in MUFAs, n-3 PUFAs, fiber, and plant protein and reduced in fructose. Before and after the intervention, intrahepatic lipids, body fat distribution, fasting and postprandial responses to a mixed-meal shake challenge test of cardiometabolic risk factors, lipoproteins, vascular measurements, and adipose tissue transcriptome were assessed.

RESULTS

The high-nutrient-quality ER diet (-8.4 ± 3.2) induced 2.1 kg more weight loss (P = 0.007) than the low-nutrient-quality ER diet (-6.3 ± 3.9), reduced fasting serum total cholesterol (P = 0.014) and plasma triglycerides (P < 0.001), promoted an antiatherogenic lipoprotein profile, and induced a more pronounced decrease in adipose tissue gene expression of energy metabolism pathways than the low-quality ER diet. Explorative analyses showed that the difference in weight loss between the two ER diets was specifically present in insulin-sensitive subjects (HOMA-IR ≤ 2.5), in whom the high-nutrient-quality diet induced 3.9 kg more weight loss than the low-nutrient-quality diet.

CONCLUSIONS

A high-nutrient-quality 25% ER diet is more beneficial for cardiometabolic health than a low-nutrient-quality 25% ER diet. Overweight, insulin-sensitive subjects may benefit more from a high- than a low-nutrient-quality ER diet with respect to weight loss, due to potential attenuation of glucose-induced lipid synthesis in adipose tissue.

Sex-specific associations of adiposity with cardiometabolic traits in the UK: A multi-life stage cohort study with repeat metabolomics

BACKGROUND

Sex differences in cardiometabolic disease risk are commonly observed across the life course but are poorly understood and may be due to different associations of adiposity with cardiometabolic risk in females and males. We examined whether adiposity is differently associated with cardiometabolic trait levels in females and males at 3 different life stages.

METHODS AND FINDINGS

Data were from 2 generations (offspring, Generation 1 [G1] born in 1991/1992 and their parents, Generation 0 [G0]) of a United Kingdom population-based birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). Follow-up continues on the cohort; data up to 25 y after recruitment to the study are included in this analysis. Body mass index (BMI) and total fat mass from dual-energy X-ray absorptiometry (DXA) were measured at mean age 9 y, 15 y, and 18 y in G1. Waist circumference was measured at 9 y and 15 y in G1. Concentrations of 148 cardiometabolic traits quantified using nuclear magnetic resonance spectroscopy were measured at 15 y, 18 y, and 25 y in G1. In G0, all 3 adiposity measures and the same 148 traits were available at 50 y. Using linear regression models, sex-specific associations of adiposity measures at each time point (9 y, 15 y, and 18 y) with cardiometabolic traits 3 to 6 y later were examined in G1. In G0, sex-specific associations of adiposity measures and cardiometabolic traits were examined cross-sectionally at 50 y. A total of 3,081 G1 and 4,887 G0 participants contributed to analyses. BMI was more strongly associated with key atherogenic traits in males compared with females at younger ages (15 y to 25 y), and associations were more similar between the sexes or stronger in females at 50 y, particularly for apolipoprotein B-containing lipoprotein particles and lipid concentrations. For example, a 1 standard deviation (SD) (3.8 kg/m2) higher BMI at 18 y was associated with 0.36 SD (95% confidence interval [CI] = 0.20, 0.52) higher concentrations of extremely large very-low-density lipoprotein (VLDL) particles at 25 y in males compared with 0.15 SD (95% CI = 0.09, 0.21) in females, P value for sex difference = 0.02. By contrast, at 50 y, a 1 SD (4.8 kg/m2) higher BMI was associated with 0.33 SD (95% CI = 0.25, 0.42) and 0.30 SD (95% CI = 0.26, 0.33) higher concentrations of extremely large VLDL particles in males and females, respectively, P value for sex difference = 0.42. Sex-specific associations of DXA-measured fat mass and waist circumference with cardiometabolic traits were similar to findings for BMI and cardiometabolic traits at each age. The main limitation of this work is its observational nature, and replication in independent cohorts using methods that can infer causality is required.

CONCLUSIONS

The results of this study suggest that associations of adiposity with adverse cardiometabolic risk begin earlier in the life course among males compared with females and are stronger until midlife, particularly for key atherogenic lipids. Adolescent and young adult males may therefore be high priority targets for obesity prevention efforts.

The causal association of polyunsaturated fatty acids with allergic disease: A two-sample Mendelian randomization study

Objectives

Previous studies have reported a potential association of polyunsaturated fatty acids (PUFAs) levels with allergic disease risk and the possible benefit of PUFAs supplementation on allergic disease prevention. This study was performed to estimate the genetic association between PUFAs and allergic diseases using the method of both univariable and multivariable two-sample Mendelian randomization (MR).

Methods

As indicators of the PUFAs levels, we included the omega-3, omega-6, docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), linoleic acid (LA), and the ratio of omega-6 to omega-3 (omega-6:3). Summarized statistics of genome-wide association studies (GWASs) for these PUFAs were obtained from the United Kingdom Biobank and the Twins United Kingdom cohort. Genetic data relating to allergic diseases, including atopic dermatitis (AD), allergic rhinitis (AR), allergic conjunctivitis (AC), allergic urticaria (AU) and asthma, were accessed from the FinnGen biobank analysis. Odds ratios and 95% CIs were used to express the impact.

Results

The MR results denoted a genetic association between the genetically determined increase in omega-3 levels and the decreased risk of some allergic diseases including AD (OR: 0.863; 95% CI: 0.785 to 0.949; p = 3.86E-03), AC (OR:0.720; 95% CI: 0.547 to 0.947; p = 1.87E-02) and AU (OR:0.821; 95% CI: 0.684 to 0.985; p = 3.42E-02), while omega-6 and DHA level was only found to have negatively correlation with risk of AC with ORs of 0.655 (95% CI: 0.445 to 0.964; p = 3.18E-02) and 0.671 (95% CI 0.490 to 0.918; p = 1.25E-02), respectively. Omega-6:3 were causally significantly associated with the increased risk of AD (OR:1.171; 95% CI: 1.045 to 1.312; p = 6.46E-03) and AC (IVW: OR:1.341; 95% CI: 1.032 to 1.743; p = 2.83E-02). After adjustment of age, economic level, BMI, smoking and alcohol behaviors in the multivariable MR analysis, a direct causal protective effect of omega-3 on AD and AC, as well as a direct causal association between DHA and AD were observed. Omega-6:3 was also found to be directly associated with an increased risk of AD and AC. No association was found of EPA or LA with allergic diseases.

Conclusion

Higher PUFA concentrations (omega-3, omega-6, DHA) and lower omega-6:3 ratios were genetically associated with a lower risk of some allergic diseases.

Sex Differences in the Metabolome of Alzheimer's Disease Progression

Alzheimer's disease (AD) is the leading cause of dementia; however, men and women face differential AD prevalence, presentation, and progression risks. Characterizing metabolomic profiles during AD progression is fundamental to understand the metabolic disruptions and the biological pathways involved. However, outstanding questions remain of whether peripheral metabolic changes occur equally in men and women with AD. Here, we evaluated differential effects of metabolomic and brain volume associations between sexes. We used three cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), evaluated 1,368 participants, two metabolomic platforms with 380 metabolites in total, and six brain segment volumes. Using dimension reduction techniques, we took advantage of the correlation structure of the brain volume phenotypes and the metabolite concentration values to reduce the number of tests while aggregating relevant biological structures. Using WGCNA, we aggregated modules of highly co-expressed metabolites. On the other hand, we used partial least squares regression-discriminant analysis (PLS-DA) to extract components of brain volumes that maximally co-vary with AD diagnosis as phenotypes. We tested for differences in effect sizes between sexes in the association between single metabolite and metabolite modules with the brain volume components. We found five metabolite modules and 125 single metabolites with significant differences between sexes. These results highlight a differential lipid disruption in AD progression between sexes. Men showed a greater negative association of phosphatidylcholines and sphingomyelins and a positive association of VLDL and large LDL with AD progression. In contrast, women showed a positive association of triglycerides in VLDL and small and medium LDL with AD progression. Explicitly identifying sex differences in metabolomics during AD progression can highlight particular metabolic disruptions in each sex. Our research study and strategy can lead to better-tailored studies and better-suited treatments that take sex differences into account.

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

Background

Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. ‘metabolomics’, is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies.

Methods

To this end, we have employed ∼26,000 ¹H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC_{5-Fold CV} = 0·94) and lipid medication usage (AUC_{5-Fold CV} = 0·90).

Findings

Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants.

Interpretation

To conclude, we provide ¹H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved.

Funding

BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).

Systems epidemiology of metabolomics measures reveals new relationships between lipoproteins and other small molecules

INTRODUCTION

The study of lipoprotein metabolism at the population level can provide valuable information for the organisation of lipoprotein related processes in the body. To use this information towards interventional hypotheses generation and testing, we need to be able to identify the mechanistic connections among the large number of observed correlations between the measured components of the system.

OBJECTIVES

To use population level metabolomics information to gain insight on their biochemical networks and metabolism.

METHODS

Genetic and metabolomics information for 230 metabolic measures, predominately lipoprotein related, from a targeted nuclear magnetic resonance approach, in two samples of an established European cohort, totalling more than 9400 individuals analysed using phenotypic and genetic correlations, as well as Mendelian Randomisation.

RESULTS

More than 20,500 phenotypic correlations were identified in the data, with almost 2000 also showing evidence of strong genetic correlation. Mendelian randomisation, provided evidence for a causal effect between 9496 pairs of metabolic measures, mainly between lipoprotein traits. The results provide insights on the organisation of lipoproteins in three distinct classes, the heterogeneity between HDL particles, and the association, or lack of, between CLA, glycolysis markers, such as glucose and citrate, and glycoproteins with lipids subfractions. Two examples for the use of the approach in systems biology of lipoproteins are presented.

CONCLUSIONS

Genetic variation can be used to infer the underlying mechanisms for the associations between lipoproteins for hypothesis generation and confirmation, and, together with biological information, to map complex biological processes.

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; P_{heterogeneity}<0.001). In contrast, triglyceride levels in low-density lipoprotein and high-density lipoprotein were associated with both end points (P_{heterogeneity}>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 (P_{heterogeneity}<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.

PCSK9 Activity Is Potentiated Through HDL Binding

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Evaluating the direct effects of childhood adiposity on adult systemic metabolism: a multivariable Mendelian randomization analysis

BACKGROUND

Individuals who are obese in childhood have an elevated risk of disease in adulthood. However, whether childhood adiposity directly impacts intermediate markers of this risk, independently of adult adiposity, is unclear. In this study, we have simultaneously evaluated the effects of childhood and adulthood body size on 123 systemic molecular biomarkers representing multiple metabolic pathways.

METHODS

Two-sample Mendelian randomization (MR) was conducted to estimate the causal effect of childhood body size on a total of 123 nuclear magnetic resonance-based metabolic markers using summary genome-wide association study (GWAS) data from up to 24 925 adults. Multivariable MR was then applied to evaluate the direct effects of childhood body size on these metabolic markers whilst accounting for adult body size. Further MR analyses were undertaken to estimate the potential mediating effects of these circulating metabolites on the risk of coronary artery disease (CAD) in adulthood using a sample of 60 801 cases and 123 504 controls.

RESULTS

Univariable analyses provided evidence that childhood body size has an effect on 42 of the 123 metabolic markers assessed (based on P < 4.07 × 10-4). However, the majority of these effects (35/42) substantially attenuated when accounting for adult body size using multivariable MR. We found little evidence that the biomarkers that were potentially influenced directly by childhood body size (leucine, isoleucine and tyrosine) mediate this effect onto adult disease risk. Very-low-density lipoprotein markers provided the strongest evidence of mediating the long-term effect of adiposity on CAD risk.

CONCLUSIONS

Our findings suggest that childhood adiposity predominantly exerts its detrimental effect on adult systemic metabolism along a pathway that involves adulthood body size.