Landscape of brain myeloid cell transcriptome along the spatiotemporal progression of Alzheimer's disease reveals distinct sequential responses to Aß and tau.

Human microglia are critically involved in Alzheimer's disease (AD) progression, as shown by genetic and molecular studies. However, their role in tau pathology progression in human brain has not been well described. Here, we characterized 32 human donors along progression of AD pathology, both in time-from early to late pathology-and in space-from entorhinal cortex (EC), inferior temporal gyrus (ITG), prefrontal cortex (PFC) to visual cortex (V2 and V1)-with biochemistry, immunohistochemistry, and single nuclei-RNA-sequencing, profiling a total of 337,512 brain myeloid cells, including microglia. While the majority of microglia are similar across brain regions, we identified a specific subset unique to EC which may contribute to the early tau pathology present in this region. We calculated conversion of microglia subtypes to diseased states and compared conversion patterns to those from AD animal models. Targeting genes implicated in this conversion, or their upstream/downstream pathways, could halt gene programs initiated by early tau progression. We used expression patterns of early tau progression to identify genes whose expression is reversed along spreading of spatial tau pathology (EC > ITG > PFC > V2 > V1) and identified their potential involvement in microglia subtype conversion to a diseased state. This study provides a data resource that builds on our knowledge of myeloid cell contribution to AD by defining the heterogeneity of microglia and brain macrophages during both temporal and regional pathology aspects of AD progression at an unprecedented resolution.

Establishment of a high-content imaging assay for tau aggregation in hiPSC-derived neurons differentiated from two protocols to routinely evaluate compounds and genetic perturbations.

Aberrant protein aggregation is a pathological cellular hallmark of many neurodegenerative diseases, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD), where the tau protein is aggregating, forming neurofibrillary tangles (NFTs), and propagating from neuron to neuron. These processes have been linked to disease progression and a decline in cognitive function. Various therapeutic approaches aim at the prevention or reduction of tau aggregates in neurons. Human induced pluripotent stem cells (hiPSCs) are a very valuable tool in neuroscience discovery, as they offer access to potentially unlimited amounts of cell types that are affected in disease, including cortical neurons, for in vitro studies. We have generated an in vitro model for tau aggregation that uses hiPSC - derived neurons expressing an aggregation prone, fluorescently tagged version of the human tau protein after lentiviral transduction. Upon addition of tau seeds in the form of recombinant sonicated paired helical filaments (sPHFs), the neurons show robust, disease-like aggregation of the tau protein. The model was developed as a plate-based high content screening assay coupled with an image analysis algorithm to evaluate the impact of small molecules or genetic perturbations on tau. We show that the assay can be used to evaluate small molecules or screen targeted compound libraries. Using siRNA-based gene knockdown, genes of interest can be evaluated, and we could show that a targeted gene library can be screened, by screening nearly 100 deubiquitinating enzymes (DUBs) in that assay. The assay uses an imaging-based readout, a relatively short timeline, quantifies the extent of tau aggregation, and also allows the assessment of cell viability. Furthermore, it can be easily adapted to different hiPSC lines or neuronal subtypes. Taken together, this complex and highly relevant approach can be routinely applied on a weekly basis in the screening funnels of several projects and generates data with a turnaround time of approximately five weeks.

SETD7-mediated monomethylation is enriched on soluble Tau in Alzheimer's disease.

BACKGROUND: Human tauopathies including Alzheimer's disease (AD) are characterized by alterations in the post-translational modification (PTM) pattern of Tau, which parallel the formation of insoluble Tau aggregates, neuronal dysfunction and degeneration. While PTMs on aggregated Tau have been studied in detail, much less is known about the modification patterns of soluble Tau. Furthermore, PTMs other than phosphorylation have only come into focus recently and are still understudied. Soluble Tau species are likely responsible for the spreading of pathology during disease progression and are currently being investigated as targets for immunotherapies. A better understanding of their biochemical properties is thus of high importance. METHODS: We used a mass spectrometry approach to characterize Tau PTMs on a detergent-soluble fraction of human AD and control brain tissue, which led to the discovery of novel lysine methylation events. We developed specific antibodies against Tau methylated at these sites and biochemically characterized methylated Tau species in extracts from human brain, the rTg4510 mouse model and in hiPSC-derived neurons. RESULTS: Our study demonstrates that methylated Tau levels increase with Tau pathology stage in human AD samples as well as in a mouse model of Tauopathy. Methylated Tau is enriched in soluble brain extracts and is not associated with hyperphosphorylated, high molecular weight Tau species. We also show that in hiPSC-derived neurons and mouse brain, methylated Tau preferentially localizes to the cell soma and nuclear fractions and is absent from neurites. Knock down and inhibitor studies supported by proteomics data led to the identification of SETD7 as a novel lysine methyltransferase for Tau. SETD7 specifically methylates Tau at K132, an event that facilitates subsequent methylation at K130. CONCLUSIONS: Our findings indicate that methylated Tau has a specific somatic and nuclear localization, suggesting that the methylation of soluble Tau species may provide a signal for their translocation to different subcellular compartments. Since the mislocalization and depletion of Tau from axons is associated with tauopathies, our findings may shed light onto this disease-associated phenomenon.

Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer's disease etiopathogenesis.

Alzheimer's disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analysed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by APOE haplotype (APOE2, APOE3 and APOE4). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterised a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in APOE2 and APOE4 AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies.

Obesity and weight loss are inversely related to mortality and cardiovascular outcome in prediabetes and type 2 diabetes: data from the ORIGIN trial.

AIMS: The association of body weight and weight change with mortality and cardiovascular (CV) outcome in patients with diabetes mellitus (DM) is not clearly established. We assessed the relationship between weight, weight change, and outcomes in patients with established CV risk factors and type 2 DM or pre-diabetes. METHODS AND RESULTS: A total of 12 521 participants from the ORIGIN trial were grouped in BMI categories of low body weight [body mass index (BMI) < 22 kg/m2] normal (22-24.9), overweight (25-29.9), obesity Grades 1-3 (30-34.9, 35-39.9, ≥40 kg/m2, respectively). Outcome variables included total and CV mortality and composite outcomes of CV death, non-fatal stroke, or myocardial infarction plus revascularization or heart failure hospitalization. Follow-up was 6.2 years (interquartile range 5.8-6.7 years). After multivariable adjustment, lowest risks were seen in patients with overweight and mild obesity for total mortality [overweight: hazard ratio (HR) 0.80 (95% confidence interval (CI) 0.69-0.91); obesity Grade 1: HR 0.82 (0.71-0.95), both P < 0.01)] and CV mortality [overweight: HR 0.79 (0.66-0.94); obesity Grade 1: 0.79 (0.65-0.95), all compared to patients with normal BMI, P < 0.05]. Obesity of any severity was not associated with higher mortality. Low body weight was related to higher mortality [HR 1.28 (1.02-1.61); CV mortality: HR 1.34 (1.01-1.79), P < 0.05]. A continued 2-year weight loss was associated with higher risk of mortality [HR 1.32 (1.18-1.46), P < 0.0001] and CV mortality [HR 1.18 (1.02-1.35), compared to patients without weight loss, P < 0.05]. In turn, weight gain was not related to any adverse outcome. CONCLUSION: Obesity in patients with DM or pre-diabetes and CV risk profile was not associated with higher mortality or adverse CV outcome. The lowest mortality risk was seen in patients with overweight and moderate obesity (BMI 25-35 kg/m2). Weight loss was an independent risk factor for higher mortality compared to no weight loss.

Sample size calculations for detecting disease-modifying osteoarthritis drug effects on the incidence of end-stage knee osteoarthritis in clinical trials: Data from the Osteoarthritis Initiative.

OBJECTIVE: We previously published data representing calculations for sample sizes assuming that the reduction of the incidence of knee joint replacement (KJR) would be an endpoint to prove efficacy of a disease-modifying drug in osteoarthritis (DMOAD). The sample sizes required for such hypothetical studies appeared to be high, rendering those studies unrealistic in the clinical research setting for practical reasons. The purpose of this work is to calculate sample sizes for hypothetical trials for DMOAD efficacy using a proxy for reaching end-stage knee osteoarthritis (esKOA) as an endpoint. METHODS: Based on a sub-population of the Osteoarthritis Initiative, the cumulative incidence for both esKOA and KJR were calculated for a period of four years. The sample sizes of hypothetical DMOAD trials were then calculated for particular sub-cohorts of the OAI subpopulation, subdividing the groups according to age, Kellgren-Lawrence (KL) grades and gender. RESULTS: Both the incidence for esKOA and for KJR over the four year period increase along with rising age, severity of OA and being female. The sample sizes to detect DMOAD efficacy are considerably smaller if reduction of the incidence of esKOA is chosen as an endpoint instead of reduction of the incidence of KJR. CONCLUSION: In the future, generating health-economic data may become increasingly important to gain reimbursement. By choosing esKOA as an endpoint in DMOAD trials, we are able to show in our work that clinical trials in the field of OA are feasible, merely including a few hundred study participants per study arm.

Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study.

Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long-term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounders such as passaging effects and progenitor storage. Single-cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis-generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.

High throughput screening of mitochondrial bioenergetics in human differentiated myotubes identifies novel enhancers of muscle performance in aged mice.

Mitochondrial dysfunction is increasingly recognized as a contributor to age-related muscle loss and functional impairment. Therefore, we developed a high throughput screening strategy that enabled the identification of compounds boosting mitochondrial energy production in a human skeletal muscle cell model. Screening of 7949 pure natural products revealed 22 molecules that significantly increased oxygen consumption and ATP levels in myotubes. One of the most potent compounds was the flavanone hesperetin. Hesperetin (10 µM) increased intracellular ATP by 33% and mitochondrial spare capacity by 25%. Furthermore, the compound reduced oxidative stress in primary myotubes as well as muscle tissue in vivo. In aged mice administration of hesperetin (50 mg/kg/d) completely reverted the age-related decrease of muscle fiber size and improved running performance of treated animals. These results provide a novel screening platform for the discovery of drugs that can improve skeletal muscle function in patients suffering from sarcopenia or other disorders associated with mitochondrial dysfunction.

Erratum: Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

A correction to this article has been published and is linked from the HTML version of this article.

Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney.

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7, TNXB, LRP12, LOC283335, SEPT9, and AKT2, and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA. Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.

Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10-8) or suggestively genome wide (p < 2.3 × 10-6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.Lean body mass is a highly heritable trait and is associated with various health conditions. Here, Kiel and colleagues perform a meta-analysis of genome-wide association studies for whole body lean body mass and find five novel genetic loci to be significantly associated.

An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans.

To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci (P < 5 × 10-8), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.

A genome-wide association meta-analysis on lipoprotein (a) concentrations adjusted for apolipoprotein (a) isoforms.

High lipoprotein (a) [Lp(a)] concentrations are an independent risk factor for cardiovascular outcomes. Concentrations are strongly influenced by apo(a) kringle IV repeat isoforms. We aimed to identify genetic loci associated with Lp(a) concentrations using data from five genome-wide association studies (n = 13,781). We identified 48 independent SNPs in the LPA and 1 SNP in the APOE gene region to be significantly associated with Lp(a) concentrations. We also adjusted for apo(a) isoforms to identify loci affecting Lp(a) levels independently from them, which resulted in 31 SNPs (30 in the LPA, 1 in the APOE gene region). Seven SNPs showed a genome-wide significant association with coronary artery disease (CAD) risk. A rare SNP (rs186696265; MAF ∼1%) showed the highest effect on Lp(a) and was also associated with increased risk of CAD (odds ratio = 1.73, P = 3.35 × 10-30). Median Lp(a) values increased from 2.1 to 91.1 mg/dl with increasing number of Lp(a)-increasing alleles. We found the APOE2-determining allele of rs7412 to be significantly associated with Lp(a) concentrations (P = 3.47 × 10-10). Each APOE2 allele decreased Lp(a) by 3.34 mg/dl corresponding to ∼15% of the population's mean values. Performing a gene-based test of association, including suspected Lp(a) receptors and regulators, resulted in one significant association of the TLR2 gene with Lp(a) (P = 3.4 × 10-4). In summary, we identified a large number of independent SNPs in the LPA gene region, as well as the APOE2 allele, to be significantly associated with Lp(a) concentrations.

Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity.

Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10-7, range P = 9.2 × 10-8 to 6.0 × 10-46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10-6, range P = 5.5 × 10-6 to 6.1 × 10-35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 × 10-54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape.

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

A genome-wide association meta-analysis on apolipoprotein A-IV concentrations.

Apolipoprotein A-IV (apoA-IV) is a major component of HDL and chylomicron particles and is involved in reverse cholesterol transport. It is an early marker of impaired renal function. We aimed to identify genetic loci associated with apoA-IV concentrations and to investigate relationships with known susceptibility loci for kidney function and lipids. A genome-wide association meta-analysis on apoA-IV concentrations was conducted in five population-based cohorts (n = 13,813) followed by two additional replication studies (n = 2,267) including approximately 10 M SNPs. Three independent SNPs from two genomic regions were significantly associated with apoA-IV concentrations: rs1729407 near APOA4 (P = 6.77 × 10 - 44), rs5104 in APOA4 (P = 1.79 × 10-24) and rs4241819 in KLKB1 (P = 5.6 × 10-14). Additionally, a look-up of the replicated SNPs in downloadable GWAS meta-analysis results was performed on kidney function (defined by eGFR), HDL-cholesterol and triglycerides. From these three SNPs mentioned above, only rs1729407 showed an association with HDL-cholesterol (P = 7.1 × 10 - 07). Moreover, weighted SNP-scores were built involving known susceptibility loci for the aforementioned traits (53, 70 and 38 SNPs, respectively) and were associated with apoA-IV concentrations. This analysis revealed a significant and an inverse association for kidney function with apoA-IV concentrations (P = 5.5 × 10-05). Furthermore, an increase of triglyceride-increasing alleles was found to decrease apoA-IV concentrations (P = 0.0078). In summary, we identified two independent SNPs located in or next the APOA4 gene and one SNP in KLKB1 The association of KLKB1 with apoA-IV suggests an involvement of apoA-IV in renal metabolism and/or an interaction within HDL particles. Analyses of SNP-scores indicate potential causal effects of kidney function and by lesser extent triglycerides on apoA-IV concentrations.

Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error.

Myopia is the most common human eye disorder and it results from complex genetic and environmental causes. The rapidly increasing prevalence of myopia poses a major public health challenge. Here, the CREAM consortium performs a joint meta-analysis to test single-nucleotide polymorphism (SNP) main effects and SNP × education interaction effects on refractive error in 40,036 adults from 25 studies of European ancestry and 10,315 adults from 9 studies of Asian ancestry. In European ancestry individuals, we identify six novel loci (FAM150B-ACP1, LINC00340, FBN1, DIS3L-MAP2K1, ARID2-SNAT1 and SLC14A2) associated with refractive error. In Asian populations, three genome-wide significant loci AREG, GABRR1 and PDE10A also exhibit strong interactions with education (P<8.5 × 10(-5)), whereas the interactions are less evident in Europeans. The discovery of these loci represents an important advance in understanding how gene and environment interactions contribute to the heterogeneity of myopia.

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research.

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase.

Determination of serum biomarkers in osteoarthritis patients: a previous interventional imaging study revisited.

To evaluate in an interventional trial on knee osteoarthritis (OA) the level and change of two serum biomarkers and their correlation with imaging parameters. The previously reported interventional OA study (ClinicalTrials.gov: NCT00536302) identified a positive effect of collagen hydrolysate (CH) on cartilage morphology in patients with knee OA using delayed gadolinium enhanced magnetic resonance imaging (dGEMRIC). It was the objective in this research project to evaluate in an interventional clinical trial on knee OA the level and change of two serum biomarkers and their correlation with imaging parameters. In blood samples of study participants, we determined the concentration of procollagen type II N-terminal propeptide (PIIANP) and aggrecan chondroitin sulfate 846 epitope (CS846) at baseline (BL) and at the follow-up (FU) visits at 24 and 48 weeks. We measured the level and change of biomarker concentrations in both study groups, and the correlation of those changes with changes in dGEMRIC. For the biomarker PIIANP, we observed a significantly greater increase in the CH group (29.9% vs. 1.2% at week 24, P= 0.001). For CS846, the mean concentration was lower among the CH treated participants at 24 weeks (78% vs. 96%, P= 0.045). Consistent correlations of changes in biomarkers PIIANP and CS846 with changes of the dGEMRIC score could not be observed. In this study, different changes per treatment group, CH and placebo were seen for dGEMRIC and PIIANP BL to 24 weeks FU, but only weak correlations between changes in dGEMRIC and biochemical markers.

Gender-specific pathway differences in the human serum metabolome.

The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.