Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under ...substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.
Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into ...clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.
Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members ...show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in type 2 diabetes (T2D). Cross-validated prediction models based on this signature similarly classified T2D. In an independent cohort (FoCus), disruption of microbial oscillation and the model for T2D classification was confirmed in 1,363 subjects. This arrhythmic risk signature was able to predict T2D in 699 KORA subjects 5 years after initial sampling, being most effective in combination with BMI. Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. Thus, a cohort-specific risk pattern of arrhythmic taxa enables classification and prediction of T2D, suggesting a functional link between circadian rhythms and the microbiome in metabolic diseases.
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•Human gut microbiome exhibits diurnal rhythmicity across populations and individuals•Obese and T2D individuals show disrupted circadian rhythms in the gut microbiome•Arrhytmic bacterial signatures contribute to risk classification and prediction of T2D•These risk signatures show regional differences in applicability across three cohorts
Reitmeier et al. show that specific gut microbes exhibit rhythmic oscillations in relative abundance and identified taxa with disrupted rhythmicity in individuals with type 2 diabetes (T2D). This arrhythmic signature contributed to the classification and prediction of T2D, suggesting functional links between circadian rhythmicity and the microbiome in metabolic diseases.
Summary Background Obesity is a major health problem that is determined by interactions between lifestyle and environmental and genetic factors. Although associations between several genetic variants ...and body-mass index (BMI) have been identified, little is known about epigenetic changes related to BMI. We undertook a genome-wide analysis of methylation at CpG sites in relation to BMI. Methods 479 individuals of European origin recruited by the Cardiogenics Consortium formed our discovery cohort. We typed their whole-blood DNA with the Infinium HumanMethylation450 array. After quality control, methylation levels were tested for association with BMI. Methylation sites showing an association with BMI at a false discovery rate q value of 0·05 or less were taken forward for replication in a cohort of 339 unrelated white patients of northern European origin from the MARTHA cohort. Sites that remained significant in this primary replication cohort were tested in a second replication cohort of 1789 white patients of European origin from the KORA cohort. We examined whether methylation levels at identified sites also showed an association with BMI in DNA from adipose tissue (n=635) and skin (n=395) obtained from white female individuals participating in the MuTHER study. Finally, we examined the association of methylation at BMI-associated sites with genetic variants and with gene expression. Findings 20 individuals from the discovery cohort were excluded from analyses after quality-control checks, leaving 459 participants. After adjustment for covariates, we identified an association (q value ≤0·05) between methylation at five probes across three different genes and BMI. The associations with three of these probes—cg22891070, cg27146050, and cg16672562, all of which are in intron 1 of HIF3A —were confirmed in both the primary and second replication cohorts. For every 0·1 increase in methylation β value at cg22891070, BMI was 3·6% (95% CI 2·4–4·9) higher in the discovery cohort, 2·7% (1·2–4·2) higher in the primary replication cohort, and 0·8% (0·2–1·4) higher in the second replication cohort. For the MuTHER cohort, methylation at cg22891070 was associated with BMI in adipose tissue (p=1·72 × 10−5 ) but not in skin (p=0·882). We observed a significant inverse correlation (p=0·005) between methylation at cg22891070 and expression of one HIF3A gene-expression probe in adipose tissue. Two single nucleotide polymorphisms—rs8102595 and rs3826795—had independent associations with methylation at cg22891070 in all cohorts. However, these single nucleotide polymorphisms were not significantly associated with BMI. Interpretation Increased BMI in adults of European origin is associated with increased methylation at the HIF3A locus in blood cells and in adipose tissue. Our findings suggest that perturbation of hypoxia inducible transcription factor pathways could have an important role in the response to increased weight in people. Funding The European Commission, National Institute for Health Research, British Heart Foundation, and Wellcome Trust.
Information regarding the variability of metabolite levels over time in an individual is required to estimate the reproducibility of metabolite measurements. In intervention studies, it is critical ...to appropriately judge changes that are elicited by any kind of intervention. The pre-analytic phase (collection, transport and sample processing) is a particularly important component of data quality in multi-center studies.
Reliability of metabolites (within-and between-person variance, intraclass correlation coefficient) and stability (shipment simulation at different temperatures, use of gel-barrier collection tubes, freeze-thaw cycles) were analyzed in fasting serum and plasma samples of 22 healthy human subjects using a targeted LC-MS approach.
Reliability of metabolite measurements was higher in serum compared to plasma samples and was good in most saturated short-and medium-chain acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids and hexose. The majority of metabolites were stable for 24 h on cool packs and at room temperature in non-centrifuged tubes. Plasma and serum metabolite stability showed good coherence. Serum metabolite concentrations were mostly unaffected by tube type and one or two freeze-thaw cycles.
A single time point measurement is assumed to be sufficient for a targeted metabolomics analysis of most metabolites. For shipment, samples should ideally be separated and frozen immediately after collection, as some amino acids and biogenic amines become unstable within 3 h on cool packs. Serum gel-barrier tubes can be used safely for this process as they have no effect on concentration in most metabolites. Shipment of non-centrifuged samples on cool packs is a cost-efficient alternative for most metabolites.
Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated ...microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.
Background
Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic ...assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.
Methods
We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.
Results
Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (
KNN
) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.
Conclusion
Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that
KNN
-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.
Summary Background The genetic cause of intellectual disability in most patients is unclear because of the absence of morphological clues, information about the position of such genes, and suitable ...screening methods. Our aim was to identify de-novo variants in individuals with sporadic non-syndromic intellectual disability. Methods In this study, we enrolled children with intellectual disability and their parents from ten centres in Germany and Switzerland. We compared exome sequences between patients and their parents to identify de-novo variants. 20 children and their parents from the KORA Augsburg Diabetes Family Study were investigated as controls. Findings We enrolled 51 participants from the German Mental Retardation Network. 45 (88%) participants in the case group and 14 (70%) in the control group had de-novo variants. We identified 87 de-novo variants in the case group, with an exomic mutation rate of 1·71 per individual per generation. In the control group we identified 24 de-novo variants, which is 1·2 events per individual per generation. More participants in the case group had loss-of-function variants than in the control group (20/51 vs 2/20; p=0·022), suggesting their contribution to disease development. 16 patients carried de-novo variants in known intellectual disability genes with three recurrently mutated genes ( STXBP1, SYNGAP1 , and SCN2A ). We deemed at least six loss-of-function mutations in six novel genes to be disease causing. We also identified several missense alterations with potential pathogenicity. Interpretation After exclusion of copy-number variants, de-novo point mutations and small indels are associated with severe, sporadic non-syndromic intellectual disability, accounting for 45–55% of patients with high locus heterogeneity. Autosomal recessive inheritance seems to contribute little in the outbred population investigated. The large number of de-novo variants in known intellectual disability genes is only partially attributable to known non-specific phenotypes. Several patients did not meet the expected syndromic manifestation, suggesting a strong bias in present clinical syndrome descriptions. Funding German Ministry of Education and Research, European Commission 7th Framework Program, and Swiss National Science Foundation.
The availability of polygenic scores for type 2 diabetes (T2D) raises the question, whether assessing family history might become redundant. However, family history not only involves shared genetics, ...but also shared environment. It was the aim of this study to assess the independent and combined effects of one family risk score (FamRS) and a polygenic score (PGS) on prevalent and incident T2D risk in a population-based study from Germany (n = 3071). The study was conducted in 2004/2005 with up to 12 years of follow-up. The FamRS takes into account not only the number of diseased first grade relatives, but also age at onset of the relatives and age of participants. 256 prevalent and additional 163 incident T2D cases were recorded. Prevalence of T2D increased sharply for those within the top quantile of the PGS distribution resulting in an OR of 19.16 (p < 2 × 10
) for the top 20% compared to the remainder of the population, independent of age, sex, BMI, physical activity and FamRS. On the other hand, having a very strong family risk compared to average was still associated with an OR of 2.78 (p = 0.001), independent of the aforementioned factors and the PGS. The PGS and FamRS were only slightly correlated (r
= 0.018). The combined contribution of both factors varied with varying age-groups, though, with decreasing influence of the PGS with increasing age. To conclude, both, genetic information and family history are relevant for the prediction of T2D risk and might be used for identification of high risk groups to personalize prevention measures.
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