BACKGROUND:Cholesterol is a main contributor to coronary artery disease (CAD). Although the genetic basis of blood cholesterol concentration is well studied, there is currently a lack of studies investigating the genetics of its precursors from de novo biosynthesis. METHODS:We conducted a genome-wide association meta-analysis, combining data from KORA, LIFE-Heart, LIFE-Adult, LURIC, the Sorbs study, and YFS, resulting in up to 10,519 individuals. We investigated 14 traits related to serum concentrations of lanosterol, desmosterol, and cholesterol. Direct and indirect effects of lanosterol on CAD were investigated with a Mendelian randomisation mediation analysis. FINDINGS:Our analysis revealed four genome-wide significant (p < 5 × 10-8) associations not previously reported in the GWAS catalogue. These include two loci without prior connection to cholesterol, associated with lanosterol (7q21.2, CYP51A1) and free cholesterol (11q14.1), and two associations with lanosterol at loci previously reported for cholesterol (5q13.3, HMGCR; 11q23.3, APO cluster). We also replicated eight loci previously reported for associations with cholesterol-related traits. Lanosterol exhibited significant total and indirect effects on CAD, but its direct effect was not significant. INTERPRETATION:We demonstrate that the investigation of intermediate phenotypes can help to functionally fine map previously reported associations for cholesterol, improving our understanding of genetic regulation of cholesterol concentrations. Further, the effect of lanosterol on CAD is probably fully mediated by total cholesterol. FUNDING:This investigation was primarily funded by the ministry for science and health of the Rhineland-Palatinate through the CoAGE graduate programme. A complete list of funding organisations is provided in the acknowledgements.

X-chromosomal genetic variants are understudied but can yield valuable insights into sexually dimorphic human traits and diseases. We performed a sex-stratified cross-ancestry X-chromosome-wide association meta-analysis of seven kidney-related traits ( n = 908,697), identifying 23 loci genome-wide significantly associated with two of the traits: 7 for uric acid and 16 for estimated glomerular filtration rate (eGFR), including four novel eGFR loci containing the functionally plausible prioritized genes ACSL4 , CLDN2 , TSPAN6 and the female-specific DRP2 . Further, we identified five novel sex-interactions, comprising male-specific effects at FAM9B and AR/EDA2R , and three sex-differential findings with larger genetic effect sizes in males at DCAF12L1 and MST4 and larger effect sizes in females at HPRT1 . All prioritized genes in loci showing significant sex-interactions were located next to androgen response elements (ARE). Five ARE genes showed sex-differential expressions. This study contributes new insights into sex-dimorphisms of kidney traits along with new prioritized gene targets for further molecular research.

Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player of lipid metabolism with higher plasma levels in women throughout their life. Statin treatment affects PCSK9 levels also showing evidence of sex-differential effects. It remains unclear whether these differences can be explained by genetics. Methods We performed genome-wide association meta-analyses (GWAS) of PCSK9 levels stratified for sex and statin treatment in six independent studies of Europeans (8936 women/11,080 men respectively 14,825 statin-free/5191 statin-treated individuals). Loci associated in one of the strata were tested for statin- and sex-interactions considering all independent signals per locus. Independent variants at the PCSK9 gene locus were then used in a stratified Mendelian Randomization analysis (cis-MR) of PCSK9 effects on low-density lipoprotein cholesterol (LDL-C) levels to detect differences of causal effects between the subgroups. Results We identified 11 loci associated with PCSK9 in at least one stratified subgroup ( p < 1.0 × 10 –6 ), including the PCSK9 gene locus and five other lipid loci: APOB , TM6SF2 , FADS1 / FADS2 , JMJD1C , and HP / HPR . The interaction analysis revealed eight loci with sex- and/or statin-interactions. At the PCSK9 gene locus, there were four independent signals, one with a significant sex-interaction showing stronger effects in men (rs693668). Regarding statin treatment, there were two significant interactions in PCSK9 missense mutations: rs11591147 had stronger effects in statin-free individuals, and rs11583680 had stronger effects in statin-treated individuals. Besides replicating known loci, we detected two novel genome-wide significant associations: one for statin-treated individuals at 6q11.1 (within KHDRBS2 ) and one for males at 12q24.22 (near KSR2 / NOS1 ), both with significant interactions. In the MR of PCSK9 on LDL-C, we observed significant causal estimates within all subgroups, but significantly stronger causal effects in statin-free subjects compared to statin-treated individuals. Conclusions We performed the first double-stratified GWAS of PCSK9 levels and identified multiple biologically plausible loci with genetic interaction effects. Our results indicate that the observed sexual dimorphism of PCSK9 and its statin-related interactions have a genetic basis. Significant differences in the causal relationship between PCSK9 and LDL-C suggest sex-specific dosages of PCSK9 inhibitors.

Abstract Objective Despite advances in the revascularisation techniques their net clinical benefit differs substantially between patients presenting with myocardial infarction (ACS) and chronic coronary syndrome (CCS) due to obstructive coronary atherosclerosis. Considering that leukocytes represents a key pathophysiological component of myocardial ischaemia, exploration of mononuclear blood cell (PBMC) gene expression profile in CCS and ACS setting could reveal clinically relevant transcriptomic signals. The aim of the present study was to identify differences in PBMC transcriptome between ACS and CCS patients which are linked to mortality after revascularisation. Methods and Results We analysed PBMC transcriptomes from719 ACS and 486 CCS patients creating discovery cohort. The results of transcriptomic analysis were verified in replication cohort (ACS: N=682 for CCS: N=489) generated based on propensity score matching. Median time from revascularisation to blood collection (IQR) was 15.68 (-4.52, 23.23) hrs. Patients with ACS showed consistent differential expression of 8173 genes (4544 upregulated, 3629 downregulated) when compared with CCS patients. The subsequent pathway analysis also showed significant enrichment of several canonical pathways involved in major cardiovascular pathologies i.e. atherosclerosis (p = 1.22-06, enrichment = 3.39), vascular endothelial growth factor production (p = 1.16-06, enrichment = 16.73), positive regulation of vascular development (p = 2.78-06, enrichment = 5.30) and heterotopic cell adhesion including gene desmocollin 2 (DSC2, p = 3.64-04, enrichment = 6.30). In der multivariate survival analysis with differentially expressed genes, increased expression of DCS2, lamin A/C (LMNA), and UGGT2, and decreased expression of SNRNP70 predicted poor survival over the median follow-up 11.35 yrs. Consistently, controlling the false discovery rate at ≤ 5% these genes enriched pathways involved in cardiomyopathies such as arrhythmogenic right-ventricular cardiomyopathy (p = 4.19x10-4, enrichment = 58.9) and collagen disease (p = 2.29x10-3, enrichment = 23.8). Conclusion We found altered PBMC expression of multiple genes in patients requiring revascularisation for ACS versus CCS. Several differentially expressed genes included in cardiomyopathy pathways predicted long-term all-cause mortality in these patients.

Background/Objectives: Hypophosphatasemia (HPE) may be temporary (tHPE) in the context of severe diseases, such as sepsis or trauma, or it may persist (pHPE), indicating an adult form of hypophosphatasia (HPP; OMIM 171760), a rare metabolic bone disorder caused by pathogenic nucleotide variants (PNVs) in the ALPL gene. The aim of this study was to analyze the role of auxiliary general biomarkers in verifying low alkaline phosphatase (ALP) serum activity level as an alert parameter for PNVs in the ALPL gene, which are indicative of HPP. In this retrospective analysis, we examined adult patients with an ALP serum activity level below 21 U/L. The cohort comprised 88 patients with temporary HPE (tHPE group) and 20 patients with persistent HPE who underwent re-examination. Genetic analysis performed on 12 pHPE patients identified PNV in the ALPL gene in 11 cases (ALPL group). Hemoglobin [HB], aspartate aminotransferase [AST], gamma-glutamyl transferase [GGT], calcium, phosphate, thyrotropin [TSH], albumin, total protein, and C-reactive protein [CRP] levels represented basic biomarkers. A comparative analysis between groups employed a Student’s t-test, and a Student’s t-test with bootstrap sampling (n = 10.000) was performed. Results: The mean HB, ALP, calcium, albumin, and total protein levels were lower in the tHPE group compared with the ALPL group (p < 0.01). AST and CRP were increased in the tHPE group (p < 0.01). The model showed an accuracy of 90% and an AUC of 0.94, which means that it can discern the two groups ~94% of the time. Conclusions: Basic biomarker evaluation effectively supports the interpretation of a decreased ALP serum activity level in the context of suspected HPP. In patients with laboratory HPE and biomarkers within reference, a PNV in the ALPL gene is highly suspected.

Aims/hypothesis As the prevalence of insulin resistance and glucose intolerance is increasing throughout the world, diabetes-induced eye diseases are a global health burden. We aim to identify distinct optical bands which are closely related to insulin and glucose metabolism, using non-invasive, high-resolution spectral domain optical coherence tomography (SD-OCT) in a large, population-based dataset. Methods The LIFE-Adult-Study randomly selected 10,000 participants from the population registry of Leipzig, Germany. Cross-sectional, standardised phenotyping included the assessment of various metabolic risk markers and ocular imaging, such as SD-OCT-derived thicknesses of ten optical bands of the retina. Global and Early Treatment Diabetic Retinopathy Study (ETDRS) subfield-specific optical retinal layer thicknesses were investigated in 7384 healthy eyes of 7384 participants from the LIFE-Adult-Study stratified by normal glucose tolerance, prediabetes (impaired fasting glucose and/or impaired glucose tolerance and/or HbA 1c 5.7–6.4% [39–47 mmol/mol]) and diabetes. The association of optical retinal band characteristics with different indices of glucose tolerance (e.g. fasting glucose, area under the glucose curve), insulin resistance (e.g. HOMA2-IR, triglyceride glucose index), or insulin sensitivity (e.g. estimated glucose disposal rate [eGDR], Stumvoll metabolic clearance rate) was determined using multivariable linear regression analyses for the individual markers adjusted for age, sex and refraction. Various sensitivity analyses were performed to validate the observed findings. Results In the study cohort, nine out of ten optical bands of the retina showed significant sex- and glucose tolerance-dependent differences in band thicknesses. Multivariable linear regression analyses revealed a significant, independent, and inverse association between markers of glucose intolerance and insulin resistance (e.g. HOMA2-IR) with the thickness of the optical bands representing the anatomical retinal outer nuclear layer (ONL, standardised β =−0.096; p <0.001 for HOMA2-IR) and myoid zone (MZ; β =−0.096; p <0.001 for HOMA2-IR) of the photoreceptors. Conversely, markers of insulin sensitivity (e.g. eGDR) positively and independently associated with ONL ( β =0.090; p <0.001 for eGDR) and MZ ( β =0.133; p <0.001 for eGDR) band thicknesses. These global associations were confirmed in ETDRS subfield-specific analyses. Sensitivity analyses further validated our findings when physical activity, neuroanatomical cell/tissue types and ETDRS subfield categories were investigated after stratifying the cohort by glucose homeostasis. Conclusions/interpretation An impaired glucose homeostasis associates with a thinning of the optical bands of retinal ONL and photoreceptor MZ. Changes in ONL and MZ thicknesses might predict early metabolic retinal alterations in diabetes. Graphical Abstract

OBJECTIVE:Vaspin (visceral adipose tissue derived serine protease inhibitor, SERPINA12) is associated with obesity-related metabolic traits, but its causative role is still elusive. The role of genetics in serum vaspin variability to establish its causal relationship with metabolically relevant traits was investigated. METHODS:A meta-analysis of genome-wide association studies for serum vaspin from six independent cohorts (N = 7446) was conducted. Potential functional variants of vaspin were included in Mendelian randomization (MR) analyses to assess possible causal pathways between vaspin and homeostasis model assessment and lipid traits. To further validate the MR analyses, data from Genotype-Tissue Expression (GTEx) were analyzed, db/db mice were treated with vaspin, and serum lipids were measured. RESULTS:A total of 468 genetic variants represented by five independent variants (rs7141073, rs1956709, rs4905216, rs61978267, rs73338689) within the vaspin locus were associated with serum vaspin (all p < 5×10-8 , explained variance 16.8%). MR analyses revealed causal relationships between serum vaspin and triglycerides, low-density lipoprotein, and total cholesterol. Gene expression correlation analyses suggested that genes, highly correlated with vaspin expression in adipose tissue, are enriched in lipid metabolic processes. Finally, in vivo vaspin treatment reduced serum triglycerides in obese db/db mice. CONCLUSIONS:The data show that serum vaspin is strongly determined by genetic variants within vaspin, which further highlight vaspin's causal role in lipid metabolism.

Supplementary Data from Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated with Pancreatic Cancer

Additional file 5: Table S4. Frequency of lipid-related publications for the PoPS+ prioritized genes.

Additional file 17: Table S9. PheWAS UKB-MVP meta-analysis results for each index lipid variant at Bonferroni threshold for multiple testing p<=3.5e-8)

Abstract Introduction and methods: The knowledge about diagnostic and prognostic value of cardiac and inflammatory biomarkers in patients with chronic coronary syndrome (CCS) is limited. To address this, we analyzed serum levels of selected biomarkers in 2536 patients with suspected CCS (35% female) from the Leipzig LIFE Heart Study who were admitted for coronary angiography. The median follow-up was 10 years. The following biomarkers were considered: high sensitive troponin T (hsTNT), N-terminal pro B-type natriuretic peptide (NT-proBNP), copeptin, high sensitive C-reactive protein (hsCRP) and interleukin-6 (IL-6). Patients were stratified according to the angiographic severity of coronary artery disease (CAD): CAD0 (no sclerosis), CAD1 (non-obstructive, i.e., stenosis < 50%), CAD2 (≥ one stenosis ≥ 50%). Group comparison (GC) included GC1: CAD0 + 1 vs. CAD2, GC2: CAD0 vs. CAD1 + 2. Using age-, sex and symptom-based pre-test probability (PTP) table for obstructive CAD (i.e. CAD 2) which was published in the current CCS guidelines, patients were further classified into the following three categories: PTP < 5% (n=559), PTP 5-15% (n=545) and PTP > 15% (n=1432). Results CAD0, CAD1, CAD2 were apparent in 999, 529, and 1008 patients, respectively. Upon adjustment for traditional risk factors (TRF) the levels of hsTNT, NT-proBNP and IL-6 showed significant difference in GC1 (hsTNT: p=2.0x10-10, NT-proBNP: p=0.049, IL6: p=0.030). In GC2 only elevated hsTNT remained significant (p=1.9x10-7). In the ROC analysis only hsTNT slightly improved the AUC for the GC1-comparison in addition to TRF (0.729 with vs 0.712 witt hsTNT, p=0.013). Within the PTP subcategories the following results were obtained: PTP < 5%: AUC 0.747 with vs 0.730 without hsTNT, p=0.017; PTP 5-15 %: AUC 0.636 with vs 0.670 without hsTNT, p<0.001; PTP > 15%: AUC 0.699 with vs 0.677 without hsTNT, p= 0.024). In PTP 5-15% subgroup TropT cut-off value < 3 pg/mL (N= 119 / 23.4%) showed sensitivity of 89.7% and specificity of 73.1% for CAD2 detection. Ten years survival in groups CAD0, CAD1, CAD2 were 88.3%, 77.3%, 72.4%, respectively. In the multivariate analysis elevated hsTNT, NT-proBNP, copeptin and IL-6 remained significant mortality predictors in CAD2 patients with hazard ratios of similar magnitude (i.e. 1.5, 1.3, 1.4 and 1.3 per unit on the log-scale of the parameters, respectively). hsCRP did not reach significance. In the model stratified into tertiles according to the effects of classical risk factors and the joint biomarker levels for except hsCRP, hazard rates were 3.13 (tertile 2 vs 1) and 11.2 (tertile 3 vs 1). Conclusions In the present study hsTNT substantially improved the detection of obstructive CAD particularly in patients with intermediate PTP 5-15%. Furthermore, the studied biomarkers enable fast and precise non-invasive prediction of mortality risk in patients with suspected CCS, allowing tailored primary and secondary CAD prevention in this high-risk group.

Journal Article Cohort Profile: The LIFE-Adult-Study Get access Christoph Engel, Christoph Engel Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Corresponding author. Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Haertelstrasse 16–18, 04107 Leipzig, Germany. E-mail: christoph.engel@imise.uni-leipzig.de https://orcid.org/0000-0002-7247-282X Search for other works by this author on: Oxford Academic PubMed Google Scholar Kerstin Wirkner, Kerstin Wirkner Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Samira Zeynalova, Samira Zeynalova Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Ronny Baber, Ronny Baber Leipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, GermanyInstitute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig Medical Center, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Hans Binder, Hans Binder Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Uta Ceglarek, Uta Ceglarek Leipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, GermanyInstitute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig Medical Center, Leipzig, Germany https://orcid.org/0000-0002-4034-5535 Search for other works by this author on: Oxford Academic PubMed Google Scholar Cornelia Enzenbach, Cornelia Enzenbach Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Michael Fuchs, Michael Fuchs Leipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, GermanyDivision Otolaryngology, Head and Neck Surgery, Phoniatrics and Audiology, University of Leipzig Medical Center, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Andreas Hagendorff, Andreas Hagendorff Department of Cardiology, University of Leipzig Medical Center, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Sylvia Henger, Sylvia Henger Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar ... Show more Andreas Hinz, Andreas Hinz Department of Medical Psychology and Medical Sociology, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Franziska G Rauscher, Franziska G Rauscher Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Matthias Reusche, Matthias Reusche Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Steffi G Riedel-Heller, Steffi G Riedel-Heller Institute of Social Medicine, Occupational Medicine and Public Health (ISAP), Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Susanne Röhr, Susanne Röhr Institute of Social Medicine, Occupational Medicine and Public Health (ISAP), Leipzig University, Leipzig, GermanyGlobal Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland Search for other works by this author on: Oxford Academic PubMed Google Scholar Julia Sacher, Julia Sacher Cognitive Neurology, University of Leipzig Medical Center, Leipzig, GermanyDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Christian Sander, Christian Sander Leipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, GermanyDepartment of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Matthias L Schroeter, Matthias L Schroeter Cognitive Neurology, University of Leipzig Medical Center, Leipzig, GermanyDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Attila Tarnok, Attila Tarnok Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyDepartment of Preclinical Development and Validation, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Regina Treudler, Regina Treudler Department of Dermatology, Venerology and Allergology, University of Leipzig Medical Center, Leipzig, GermanyLeipzig Interdisciplinary Allergy Center (LICA)—Comprehensive Allergy Center, University of Leipzig Medical Center, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Arno Villringer, Arno Villringer Cognitive Neurology, University of Leipzig Medical Center, Leipzig, GermanyDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany https://orcid.org/0000-0003-2604-2404 Search for other works by this author on: Oxford Academic PubMed Google Scholar Rolf Wachter, Rolf Wachter Clinic and Policlinic for Cardiology, University of Leipzig Medical Center, Leipzig, Germany https://orcid.org/0000-0003-2231-2200 Search for other works by this author on: Oxford Academic PubMed Google Scholar A Veronica Witte, A Veronica Witte Cognitive Neurology, University of Leipzig Medical Center, Leipzig, GermanyDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Joachim Thiery, Joachim Thiery Leipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, GermanyInstitute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig Medical Center, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar Markus Scholz, Markus Scholz Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany https://orcid.org/0000-0002-4059-1779 Search for other works by this author on: Oxford Academic PubMed Google Scholar Markus Loeffler, Markus Loeffler Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, GermanyLeipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar LIFE-Adult-Study working group LIFE-Adult-Study working group Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 52, Issue 1, February 2023, Pages e66–e79, https://doi.org/10.1093/ije/dyac114 Published: 28 May 2022 Article history Received: 21 September 2021 Editorial decision: 26 April 2022 Accepted: 10 May 2022 Published: 28 May 2022

Additional file 18: Table S10. Lambda GC values across minor allele frequency bins for sex-specific meta-analyses.

Investigating the cross talk of different omics layers is crucial to understand molecular pathomechanisms of metabolic diseases like obesity. Here, we present a large-scale association meta-analysis of genome-wide whole blood and peripheral blood mononuclear cell (PBMC) gene expressions profiled with Illumina HT12v4 microarrays and metabolite measurements from dried blood spots (DBS) characterized by targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) in three large German cohort studies with up to 7706 samples. We found 37,295 associations comprising 72 amino acids (AA) and acylcarnitine (AC) metabolites (including ratios) and 8579 transcripts. We applied this catalogue of associations to investigate the impact of associating transcript-metabolite pairs on body mass index (BMI) as an example metabolic trait. This is achieved by conducting a comprehensive mediation analysis considering metabolites as mediators of gene expression effects and vice versa. We discovered large mediation networks comprising 27,023 potential mediation effects within 20,507 transcript-metabolite pairs. Resulting networks of highly connected (hub) transcripts and metabolites were leveraged to gain mechanistic insights into metabolic signaling pathways. In conclusion, here, we present the largest available multi-omics integration of genome-wide transcriptome data and metabolite data of amino acid and fatty acid metabolism and further leverage these findings to characterize potential mediation effects towards BMI proposing candidate mechanisms of obesity and related metabolic diseases. KEY MESSAGES: Thousands of associations of 72 amino acid and acylcarnitine metabolites and 8579 genes expand the knowledge of metabolome-transcriptome associations. A mediation analysis of effects on body mass index revealed large mediation networks of thousands of obesity-related gene-metabolite pairs. Highly connected, potentially mediating hub genes and metabolites enabled insight into obesity and related metabolic disease pathomechanisms.

Additional file 23: Table S15. Comparison of the sex-specific effects.

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (n DM = 178,691, n noDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN ) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.

Context Various clinical factors influencing serum levels of insulin-like growth factor I (IGF-I) and its binding protein 3 (IGFBP-3) are not entirely consistently described. Objective We asked whether body mass index (BMI), contraceptive drugs (CDs), and hormone replacement therapy (HRT) have potential effects on data for interpreting new age-, sex-, and puberty-adjusted reference ranges for IGF-I and IGFBP-3 serum levels. Design and Setting Subjects were mainly participants from 2 population-based cohort studies: the LIFE Child study of children and adolescents and the LIFE Adult study. Participants We investigated 9400 serum samples from more than 7000 healthy and 1278 obese subjects between 3 months and 81 years old. Main Outcome Measures Associations between IGF-I or IGFBP-3, measured with a new electrochemiluminescence immunoassay, and the predictors BMI and CDs were estimated using hierarchical linear modeling. Results During infancy, obese children had up to 1 SD score (SDS) higher mean predicted IGF-I values, converging with levels of normal-weight subjects up to 13 years old. Between 20 and 40 years of age, obesity was related to up to -0.5 lower IGF-I SDS values than the predicted values. Obesity had less impact on IGFBP-3. Estrogen- and progestin-based CDs, but not HRT, decreased IGF-I and increased IGFBP-3 (P < 0.01) in adolescents (beta (IGF-I )= -0.45, beta (IGFBP-3 )= 0.94) and adults (beta (IGF-I) = -0.43, beta (IGFBP-3 )= 1.12). Conversely, progestin-based CDs were significantly positive associated with IGF-I (beta (IGF-I )=0.82). Conclusions BMI and CDs must be considered when assessing and interpreting the clinical relevance of IGF-I and IGFBP-3 measurements.

Background For many drugs, mechanisms of action with regard to desired effects and/or unwanted side effects are only incompletely understood. To investigate possible pleiotropic effects and respective molecular mechanisms, we describe here a catalogue of commonly used drugs and their impact on the blood transcriptome. Methods and results From a population-based cohort in Germany (LIFE-Adult), we collected genome-wide gene-expression data in whole blood using in Illumina HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per individual). Expression profiles were correlated with the intake of active substances as assessed by participants’ medication. This resulted in a catalogue of fourteen substances that were identified as associated with differential gene expression for a total of 534 genes. As an independent replication cohort, an observational study of patients with suspected or confirmed stable coronary artery disease (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene expression probes per individual) was employed. Notably, we were able to replicate differential gene expression for three active substances affecting 80 genes in peripheral blood mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38). Additionally, using gene ontology enrichment analysis, we demonstrated for timolol a significant enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B . In the case of carvedilol, we showed that, beside genes with well-established association with hypertension ( GPER1 , PDE4B and TNFAIP3 ), the drug also affects genes that are only indirectly linked to hypertension due to their effects on artery walls or their role in lipid biosynthesis. Conclusions Our developed catalogue of blood gene expressions profiles affected by medication can be used to support both, drug repurposing and the identification of possible off-target effects.