Genetic determinants of serum vitamin B12 and their relation to body mass index

Lower serum vitamin B12 levels have been related to adverse metabolic health profiles, including adiposity. We used a Mendelian randomization design to test whether this relation might be causal. We included two Danish population-based studies (n(total) = 9311). Linear regression was used to test for associations between (1) serum vitamin B12 levels and body mass index (BMI), (2) genetic variants and serum vitamin B12 levels, and (3) genetic variants and BMI. The effect of a genetically determined decrease in serum vitamin B12 on BMI was estimated by instrumental variable regression. Decreased serum vitamin B12 associated with increased BMI (P < 1 × 10(−4)). A genetic risk score based on eight vitamin B12 associated variants associated strongly with serum vitamin B12 (P < 2 × 10(−43)), but not with BMI (P = 0.91). Instrumental variable regression showed that a 20% decrease in serum vitamin B12 was associated with a 0.09 kg/m(2) (95% CI 0.05; 0.13) increase in BMI (P = 3 × 10(−5)), whereas a genetically induced 20% decrease in serum vitamin B12 had no effect on BMI [−0.03 (95% CI −0.22; 0.16) kg/m(2)] (P = 0.74). Nevertheless, the strongest serum vitamin B12 variant, FUT2 rs602662, which was excluded from the B12 genetic risk score due to potential pleiotropic effects, showed a per allele effect of 0.15 kg/m(2) (95% CI 0.01; 0.32) on BMI (P = 0.03). This association was accentuated including two German cohorts (n(total) = 5050), with a combined effect of 0.19 kg/m(2) (95% CI 0.08; 0.30) (P = 4 × 10(−4)). We found no support for a causal role of decreased serum vitamin B12 levels in obesity. However, our study suggests that FUT2, through its regulation of the cross-talk between gut microbes and the human host, might explain a part of the observational association between serum vitamin B12 and BMI. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10654-016-0215-x) contains supplementary material, which is available to authorized users

Bioactive oxylipins in type 2 diabetes mellitus patients with and without hypertriglyceridemia

ObjectiveDyslipidemia, in particular elevated triglycerides (TGs) contribute to increased cardiovascular risk in type 2 diabetes mellitus (T2DM). In this pilot study we aimed to assess how increased TGs affect hepatic fat as well as polyunsaturated fatty acid (PUFA) metabolism and oxylipin formation in T2DM patients.Methods40 patients with T2DM were characterized analyzing routine lipid blood parameters, as well as medical history and clinical characteristics. Patients were divided into a hypertriglyceridemia (HTG) group (TG ≥ 1.7mmol/l) and a normal TG group with TGs within the reference range (TG &lt; 1.7mmol/l). Profiles of PUFAs and their oxylipins in plasma were measured by gas chromatography and liquid chromatography/tandem mass spectrometry. Transient elastography (TE) was used to assess hepatic fat content measured as controlled attenuation parameter (CAP) (in dB/m) and the degree of liver fibrosis measured as stiffness (in kPa).ResultsMean value of hepatic fat content measured as CAP as well as body mass index (BMI) were significantly higher in patients with high TGs as compared to those with normal TGs, and correlation analysis showed higher concentrations of TGs with increasing CAP and BMI scores in patients with T2DM. There were profound differences in plasma oxylipin levels between these two groups. Cytochrome P450 (CYP) and lipoxygenase (LOX) metabolites were generally more abundant in the HTG group, especially those derived from arachidonic acid (AA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), γ-linolenic acid (γ-LA), and α-linolenic acid (α-LA), and a strong correlation between TG levels and plasma metabolites from different pathways was observed.ConclusionsIn adult patients with T2DM, elevated TGs were associated with increased liver fat and BMI. Furthermore, these patients also had significantly higher plasma levels of CYP- and LOX- oxylipins, which could be a novel indicator of increased inflammatory pathway activity, as well as a novel target to dampen this activity

Urinary metabolomics reveals glycemic and coffee associated signatures of thyroid function in two population-based cohorts

Triiodothyronine (T3) and thyroxine (T4) as the main secretion products of the thyroid affect nearly every human tissue and are involved in a broad range of processes ranging from energy expenditure and lipid metabolism to glucose homeostasis. Metabolomics studies outside the focus of clinical manifest thyroid diseases are rare. The aim of the present investigation was to analyze the cross-sectional and longitudinal associations of urinary metabolites with serum free T4 (FT4) and thyroid-stimulating hormone (TSH).Urine Metabolites of participants of the population-based studies Inter99 (n = 5620) and Health2006/Health2008 (n = 3788) were analyzed by 1H-NMR spectroscopy. Linear or mixed linear models were used to detect associations between urine metabolites and thyroid function.Cross-sectional analyses revealed positive relations of alanine, trigonelline and lactic acid with FT4 and negative relations of dimethylamine, glucose, glycine and lactic acid with log(TSH). In longitudinal analyses, lower levels of alanine, dimethylamine, glycine, lactic acid and N,N-dimethylglycine were linked to a higher decline in FT4 levels over time, whereas higher trigonelline levels were related to a higher FT4 decline. Moreover, the risk of hypothyroidism was higher in subjects with high baseline trigonelline or low lactic acid, alanine or glycine values.The detected associations mainly emphasize the important role of thyroid hormones in glucose homeostasis. In addition, the predictive character of these metabolites might argue for a potential feedback of the metabolic state on thyroid function. Besides known metabolic consequences of TH, the link to the urine excretion of trigonelline, a marker of coffee consumption, represents a novel finding of this study and given the ubiquitous consumption of coffee requires further research

Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality

Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR analysis of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases

Genome-wide association study of 1,5-anhydroglucitol identifies novel genetic loci linked to glucose metabolism

1,5-anhydroglucitol (1,5-AG) is a biomarker of hyperglycemic excursions associated with diabetic complications. Because of its structural similarity to glucose, genetic studies of 1,5-AG can deliver complementary insights into glucose metabolism. We conducted genome-wide association studies of serum 1,5-AG concentrations in 7,550 European ancestry (EA) and 2,030 African American participants (AA) free of diagnosed diabetes from the ARIC Study. Seven loci in/near EFNA1/SLC50A1, MCM6/LCT, SI, MGAM, MGAM2, SLC5A10, and SLC5A1 showed genome-wide significant associations (P < 5 × 10-8) among EA participants, five of which were novel. Six of the seven loci were successfully replicated in 8,790 independent EA individuals, and MCM6/LCT and SLC5A10 were also associated among AA. Most of 1,5-AG-associated index SNPs were not associated with the clinical glycemic markers fasting glucose or theHbA1c, and vice versa. Only the index variant in SLC5A1 showed a significant association with fasting glucose in the expected opposing direction. Products of genes in all 1,5-AG-associated loci have known roles in carbohydrate digestion and enteral or renal glucose transport, suggesting that genetic variants associated with 1,5-AG influence its concentration via effects on glucose metabolism and handling

Rare and common genetic determinants of metabolic individuality and their effects on human health

Garrod’s concept of ‘chemical individuality’ has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant–metabolite associations (P < 1.25 × 10−11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant–metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics. Here we examine a large cohort (the INTERVAL study; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores

Lowering of Circulating Sclerostin May Increase Risk of Atherosclerosis and Its Risk Factors: Evidence From a Genome-Wide Association Meta-Analysis Followed by Mendelian Randomization

OBJECTIVE: In this study, we aimed to establish the causal effects of lowering sclerostin, target of the antiosteoporosis drug romosozumab, on atherosclerosis and its risk factors. METHODS: A genome-wide association study meta-analysis was performed of circulating sclerostin levels in 33,961 European individuals. Mendelian randomization (MR) was used to predict the causal effects of sclerostin lowering on 15 atherosclerosis-related diseases and risk factors. RESULTS: We found that 18 conditionally independent variants were associated with circulating sclerostin. Of these, 1 cis signal in SOST and 3 trans signals in B4GALNT3, RIN3, and SERPINA1 regions showed directionally opposite signals for sclerostin levels and estimated bone mineral density. Variants with these 4 regions were selected as genetic instruments. MR using 5 correlated cis-SNPs suggested that lower sclerostin increased the risk of type 2 diabetes mellitus (DM) (odds ratio [OR] 1.32 [95% confidence interval (95% CI) 1.03-1.69]) and myocardial infarction (MI) (OR 1.35 [95% CI 1.01-1.79]); sclerostin lowering was also suggested to increase the extent of coronary artery calcification (CAC) (β = 0.24 [95% CI 0.02-0.45]). MR using both cis and trans instruments suggested that lower sclerostin increased hypertension risk (OR 1.09 [95% CI 1.04-1.15]), but otherwise had attenuated effects. CONCLUSION: This study provides genetic evidence to suggest that lower levels of sclerostin may increase the risk of hypertension, type 2 DM, MI, and the extent of CAC. Taken together, these findings underscore the requirement for strategies to mitigate potential adverse effects of romosozumab treatment on atherosclerosis and its related risk factors

Variation in the SERPINA6SERPINA1 locusalters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expressionin peripheral tissues, and risk of cardiovascular disease

The stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variation