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Background: Antimicrobial autoantigenic glycoprotein 2 (GP2) is an important component of the innate immune system which originates from the exocrine pancreas as well as from the small intestines. The relationship of GP2 with the intestinal microbiome as well as the systemic implications of increased fecal GP2 levels are, however, still unclear. Therefore, fecal samples from 2,812 individuals of the Study of Health in Pomerania (SHIP) were collected to determine GP2 levels (enzyme-linked immunosorbent assay) and gut microbiota profiles (16 S rRNA gene sequencing). These data were correlated and associated with highly standardised and comprehensive phenotypic data of the study participants., Results: Fecal GP2 levels were increased in individuals with higher body mass index and smokers, whereas lower levels were found in case of preserved exocrine pancreatic function, female sex or a healthier diet. Moreover, higher GP2 levels were associated with increased serum levels of high-sensitivity C-reactive protein, loss of gut microbial diversity and an increase of potentially detrimental bacteria (Streptococcus, Haemophilus, Clostridium XIVa, or Collinsella). At the same time, predicted microbial pathways for the biosynthesis of beneficial short-chain fatty acids or lactic acid were depleted in individuals with high fecal GP2. Of note, GP2 exhibited a stronger association to overall microbiome variation than calprotectin., Conclusion: Fecal GP2 is a biomarker of gut microbiota dysbiosis and associated with increased systemic inflammation. The intestines may be more important as origin for GP2 than pancreatic acinar cells. Future studies need to investigate the potential clinical value in disease specific patient cohorts., (© 2024. The Author(s).)

Background: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways., Methods: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed., Results: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism., Conclusion: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology., (© 2023. The Author(s).)

Background: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture., Results: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories., Conclusion: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.

Background: Psoriatic Arthritis (PsA) is a chronic inflammatory disease of the joints. PsA is etiologically complex, and 11 susceptibility loci have been identified so far. Most of these overlap with loci associated with psoriasis vulgaris (PsV), the most common psoriatic skin manifestation which is also frequently seen in PsA patients. In addition, two copy number variants (CNVs) are associated with PsV, one of which, located within the LCE3 gene cluster, is also associated with PsA. Finally, an intergenic deletion has been reported as a PsA-specific CNV., Methods: We performed a genome-wide association study (GWAS) of CNVs in PsA and assessed the contribution to disease risk by CNVs at known psoriasis susceptibility loci., Results: After stringent quality assessment and validation of CNVs of the GWAS with an alternative quantitative method, two significantly associated CNVs remained, one near UXS1, the other one at the TRB locus. However, MLPA analysis did not confirm the CN state in ~1/3 of individuals, and an analysis of an independent case-control-study failed to confirm the initial associations. Furthermore, detailed PCR-based analysis of the sequence at TRB revealed the existence of a more complex genomic sequence most accurately represented by freeze hg18 which accordingly failed to confirm the hg19 sequence. Only rare CNVs were detected at psoriasis susceptibility loci. At three of 12 susceptibility loci with CNVs (CSMD1, IL12B, RYR2), CN variability was confirmed independently by MLPA. Overall, the rate of CNV confirmation by MLPA was strongly dependent upon CNV type, CNV size and the number of array markers involved in a CNV., Conclusion: Although we identified PsA associations at several loci and confirmed that the common CNVs at these sites were real, ~1/3 of the common CNV states could not be reproduced. Furthermore, replication analysis failed to confirm the original association. Furthermore, SNP array-based analyses of CNVs were found to be more reliable for deletions than duplications, independent of the respective CNV allele frequency. CNVs are thus good candidate disease variants, while the methods to detect them should be applied cautiously and reproduced by an independent method.

Background: Determinations of thyrotropin (TSH) and free thyroxine (FT 4 ) represent the gold standard in evaluation of thyroid function. To screen for novel peripheral biomarkers of thyroid function and to characterize FT 4 -associated physiological signatures in human plasma we used an untargeted OMICS approach in a thyrotoxicosis model., Methods: A sample of 16 healthy young men were treated with levothyroxine for 8 weeks and plasma was sampled before the intake was started as well as at two points during treatment and after its completion, respectively. Mass spectrometry-derived metabolite and protein levels were related to FT 4 serum concentrations using mixed-effect linear regression models in a robust setting. To compile a molecular signature discriminating between thyrotoxicosis and euthyroidism, a random forest was trained and validated in a two-stage cross-validation procedure., Results: Despite the absence of obvious clinical symptoms, mass spectrometry analyses detected 65 metabolites and 63 proteins exhibiting significant associations with serum FT 4 . A subset of 15 molecules allowed a robust and good prediction of thyroid hormone function (AUC = 0.86) without prior information on TSH or FT 4 . Main FT 4 -associated signatures indicated increased resting energy expenditure, augmented defense against systemic oxidative stress, decreased lipoprotein particle levels, and increased levels of complement system proteins and coagulation factors. Further association findings question the reliability of kidney function assessment under hyperthyroid conditions and suggest a link between hyperthyroidism and cardiovascular diseases via increased dimethylarginine levels., Conclusion: Our results emphasize the power of untargeted OMICs approaches to detect novel pathways of thyroid hormone action. Furthermore, beyond TSH and FT 4 , we demonstrated the potential of such analyses to identify new molecular signatures for diagnosis and treatment of thyroid disorders. This study was registered at the German Clinical Trials Register (DRKS) [DRKS00011275] on the 16th of November 2016.

Background: Genome-wide association studies of Single Nucleotide Polymorphisms (SNPs) have identified 55 SNPs associated with lung function. However, little is known about the effect of copy number variants (CNVs) on lung function, although CNVs represent a significant proportion of human genetic polymorphism. To assess the effect of CNVs on lung function quantitative traits, we measured copy number at 2788 previously characterised, common copy number variable regions in 6 independent cohorts (n = 24,237) using intensity data from SNP genotyping experiments. We developed a pipeline for genome-wide association analysis and meta-analysis of CNV genotypes measured across multiple studies using SNP genotype array intensity data from different platform technologies. We then undertook cohort-level genome-wide association studies of CNV with lung function in a subset of 4 cohorts (n < =12,403) with lung function measurements and meta-analysed the results. Follow-up was undertaken for CNVs which were well tagged by SNPs, in up to 146,871 individuals., Results: We generated robust copy number calls for 1962 out of 2788 (70 %) known CNV regions genome-wide, with 1103 measured with compatible class frequencies in at least 2 cohorts. We report a novel CNV association (discovery P = 0.0007) with Forced Vital Capacity (FVC) downstream of BANP on chromosome 16 that shows evidence of replication by a tag SNP in two independent studies (replication P = 0.004). In addition, we provide suggestive evidence (discovery P = 0.0002) for a role of complex copy number variation at a previously reported lung function locus, containing the rootletin gene CROCC, that is not tagged by SNPs., Conclusions: We demonstrate how common CNV regions can be reliably and consistently called across cohorts, using an existing calling algorithm and rigorous quality control steps, using SNP genotyping array intensity data. Although many common biallelic CNV regions were well-tagged by common SNPs, we also identified associations with untagged mulitallelic CNV regions thereby illustrating the potential of our approach to identify some of the missing heritability of complex traits.

Background: Hepatic steatosis confers an increased risk of metabolic and cardiovascular disease and higher health services use. Associations of the single nucleotide polymorphisms (SNP) PNPLA3 rs738409 and TM6SF2 rs58542926 with hepatic steatosis have recently been established. This study investigates the association between rs738409 and rs58542926 with health services utilization in a general population., Methods: Data of 3759 participants from Study of Health in Pomerania (SHIP), a population-based study in Germany, were obtained. The annual number of outpatient visits, hospitalization and length of hospital stay was regressed on rs738409 and rs58542926 and adjusted for socio-economic factors, lifestyle habits, clinical factors, and health status., Results: Minor allele homozygous subjects of rs738409 had an increased odds of hospitalization as compared to major allele homozygous subjects (odds ratio [OR] 1.51; 95% confidence interval [CI], 1.02 to 2.15). Heterozygous subjects did not differ from major allele homozygous subjects with respect to their odds of hospitalization. The three genotype groups of rs738409 were similar with respect to the number of outpatient visits and inpatient days. Minor allele homozygous and heterozygous subjects of rs58542926 had higher outpatient utilization (+53.04% and +67.56%, p < 0.05, respectively) and inpatient days than major allele homozygous subjects., Conclusions: After adjustment for several confounding factors, PNPLA3 rs738409 and TM6SF2 rs58542926 were associated with the number of outpatient visits, hospitalization, and inpatient days. Further studies are warranted to replicate our findings and to evaluate whether genetic data can be used to identify subjects with excess health services utilization.

Background: Obesity, defined as pathologically increased body mass index (BMI), is strongly related to an increased risk for numerous common cardiovascular and metabolic diseases. It is particularly associated with insulin resistance, hyperglycemia, and systemic oxidative stress and represents the most important risk factor for type 2 diabetes (T2D). However, the pathophysiological mechanisms underlying these associations are still not completely understood. Therefore, in order to identify potentially disease-relevant BMI-associated gene expression signatures, a transcriptome-wide association study (TWAS) on BMI was performed., Methods: Whole-blood mRNA levels determined by array-based transcriptional profiling were correlated with BMI in two large independent population-based cohort studies (KORA F4 and SHIP-TREND) comprising a total of 1977 individuals., Results: Extensive alterations of the whole-blood transcriptome were associated with BMI: More than 3500 transcripts exhibited significant positive or negative BMI-correlation. Three major whole-blood gene expression signatures associated with increased BMI were identified. The three signatures suggested: i) a ratio shift from mature erythrocytes towards reticulocytes, ii) decreased expression of several genes essentially involved in the transmission and amplification of the insulin signal, and iii) reduced expression of several key genes involved in the defence against reactive oxygen species (ROS)., Conclusions: Whereas the first signature confirms published results, the other two provide possible mechanistic explanations for well-known epidemiological findings under conditions of increased BMI, namely attenuated insulin signaling and increased oxidative stress. The putatively causative BMI-dependent down-regulation of the expression of numerous genes on the mRNA level represents a novel finding. BMI-associated negative transcriptional regulation of insulin signaling and oxidative stress management provide new insights into the pathogenesis of metabolic syndrome and T2D.

Background: Non-cellular blood circulating microRNAs (plasma miRNAs) represent a promising source for the development of prognostic and diagnostic tools owing to their minimally invasive sampling, high stability, and simple quantification by standard techniques such as RT-qPCR. So far, the majority of association studies involving plasma miRNAs were disease-specific case-control analyses. In contrast, in the present study, plasma miRNAs were analysed in a sample of 372 individuals from a population-based cohort study, the Study of Health in Pomerania (SHIP)., Methods: Quantification of miRNA levels was performed by RT-qPCR using the Exiqon Serum/Plasma Focus microRNA PCR Panel V3.M covering 179 different miRNAs. Of these, 155 were included in our analyses after quality-control. Associations between plasma miRNAs and the phenotypes age, body mass index (BMI), and sex were assessed via a two-step linear regression approach per miRNA. The first step regressed out the technical parameters and the second step determined the remaining associations between the respective plasma miRNA and the phenotypes of interest., Results: After regressing out technical parameters and adjusting for the respective other two phenotypes, 7, 15, and 35 plasma miRNAs were significantly (q < 0.05) associated with age, BMI, and sex, respectively. Additional adjustment for the blood cell parameters identified 12 and 19 miRNAs to be significantly associated with age and BMI, respectively. Most of the BMI-associated miRNAs likely originate from liver. Sex-associated differences in miRNA levels were largely determined by differences in blood cell parameters. Thus, only 7 as compared to originally 35 sex-associated miRNAs displayed sex-specific differences after adjustment for blood cell parameters., Conclusions: These findings emphasize that circulating miRNAs are strongly impacted by age, BMI, and sex. Hence, these parameters should be considered as covariates in association studies based on plasma miRNA levels. The established experimental and computational workflow can now be used in future screening studies to determine associations of plasma miRNAs with defined disease phenotypes.

Background: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study., Methods: We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits., Results: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules., Conclusions: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.

Background: It has been well-established, both by population genetics theory and direct observation in many organisms, that increased genetic diversity provides a survival advantage. However, given the limitations of both sample size and genome-wide metrics, this hypothesis has not been comprehensively tested in human populations. Moreover, the presence of numerous segregating small effect alleles that influence traits that directly impact health directly raises the question as to whether global measures of genomic variation are themselves associated with human health and disease., Results: We performed a meta-analysis of 17 cohorts followed prospectively, with a combined sample size of 46,716 individuals, including a total of 15,234 deaths. We find a significant association between increased heterozygosity and survival (P = 0.03). We estimate that within a single population, every standard deviation of heterozygosity an individual has over the mean decreases that person's risk of death by 1.57%., Conclusions: This effect was consistent between European and African ancestry cohorts, men and women, and major causes of death (cancer and cardiovascular disease), demonstrating the broad positive impact of genomic diversity on human survival.

Background: Genome-wide association studies (GWAS) using array-based genotyping technology are widely used to identify genetic loci associated with complex diseases or other phenotypes. The costs of GWAS projects based on individual genotyping are still comparatively high and increase with the size of study populations. Genotyping using pooled DNA samples, as also being referred as to allelotyping approach, offers an alternative at affordable costs. In the present study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared., Results: Regardless of the intensity correction method applied, the pooling-specific error of the pool intensity values was larger for single pools than for the comparison of the intensity values of two pools, which reflects the scenario of a case-control study. Using 50 pooled samples and analyzing 10,000 SNPs with a minor allele frequency of >1% and applying the best correction method for the corresponding type of comparison, the 90% quantile (median) of the pooling-specific absolute error of the RAS values for single sub-pools and the SNP-specific difference in allele frequency comparing two pools was 0.064 (0.026) and 0.056 (0.021), respectively., Conclusions: Correction of the RAS values reduced the error of the RAS values when analyzing single pool intensities. We developed a new correction method with high accuracy but low computational costs. Correction of RAS, however, only marginally reduced the error of true differences between two sample groups and those obtained by allelotyping. Exclusion of SNPs with a minor allele frequency of ≤ 1% notably reduced the pooling-specific error. Our findings allow for improving the estimation of the pooling-specific error and may help in designing allelotyping studies using the Affymetrix Genome-Wide Human SNP Array 6.0.

Background: The human chromosomal region 9p21.3 has been shown to be strongly associated with Coronary Heart Disease (CHD) in several Genome-wide Association Studies (GWAS). Recently, this region has also been shown to be associated with Aggressive Periodontitis (AgP), strengthening the hypothesis that the established epidemiological association between periodontitis and CHD is caused by a shared genetic background, in addition to common environmental and behavioural risk factors. However, the size of the analyzed cohorts in this primary analysis was small compared to other association studies on complex diseases. Using our own AgP cohort, we attempted to confirm the described associations for the chromosomal region 9p21.3., Methods: We analyzed our cohort consisting of patients suffering from the most severe form of AgP, generalized AgP (gAgP) (n = 130) and appropriate periodontally healthy control individuals (n = 339) by genotyping four tagging SNPs (rs2891168, rs1333042, rs1333048 and rs496892), located in the chromosomal region 9p21.3, that have been associated with AgP., Results: The results confirmed significant associations between three of the four SNPs and gAgP. The combination of our results with those from the study which described this association for the first time in a meta-analysis of the four tagging SNPs produced clearly lower p-values compared with the results of each individual study. According to these results, the most plausible genetic model for the association of all four tested SNPs with gAgP seems to be the multiplicative one., Conclusion: We positively replicated the finding of an association between the chromosomal region 9p21.3 and gAgP. This result strengthens support for the hypothesis that shared susceptibility genes within this chromosomal locus might be involved in the pathogenesis of both CHD and gAgP.