Aging | doi:10.18632/aging.100874. Jonas Zierer, Gabi Kastenmüller, Karsten Suhre, Christian Gieger, Veryan Codd, Pei-Chien Tsai, Jordana Bell, Annette Peters, Konstantin Strauch, Holger Schulz, Stephan Weidinger, Robert P. Mohney, Nilesh J. Samani, Tim Spector, Massimo Mangino, Cristina Menni
Ianalumab, an afuscosylated monoclonal antibody, depletes B cells by enhanced cellular elimination and blocks survival signals mediated by the B-cell–activating factor receptor (BAFFR). Sjögren's disease (SjD) is an autoimmune disorder with exocrine glandular and extraglandular manifestations, elevated BAFF and autoantibodies to nuclear antigens. A phase 2b dose-finding trial of ianalumab (NCT02962895) in 190 patients with active Sjögren's disease met its primary endpoint; 300 mg was clinically efficacious and improved whole salivary flow.
Objectives:
To explore changes in serum and saliva protein concentrations in patients with SjD with an aim to characterise biomarkers associated with disease activity and clinical dose response with ianalumab treatment.
Methods:
Patients with active SjD (N=190) were randomised (1:1:1:1) to receive placebo or ianalumab (5, 50, or 300 mg). Serum and saliva samples collected at the baseline and week 24 were used for protein profiling and delineation of interferon protein signatures (IFNPS) using the SomaScan(R) v4.1 platform. Levels of autoantibodies, BAFF, B-cell maturation antigen (BCMA), C-X-C motif chemokine ligand 13 (CXCL13) and CCL21 were assessed by immunoassays. A linear mixed-effect model was used to identify longitudinal changes in protein concentration between placebo and treatment groups at week 24 versus baseline. Results were visualised using heatmaps and hierarchical clustering.
Results:
At baseline, cluster analysis did not reveal significant correlations between ESSDAI scores (including subdomains) and levels of autoantibodies or proteins of interest. Ianalumab led to improved salivary flow (Figure 1a) and decreased autoantibody levels, accompanied by change in serum protein levels. The 300-mg dose significantly modulated 42 serum proteins compared to 20 proteins with 50-mg dose and 9 proteins with 5-mg dose after 24 weeks of treatment. This serum protein signature includes B-cell–related markers that were downregulated by ianalumab in a dose-dependent manner. In particular, BCMA expressed by antibody-producing cells, and the chemokines CXCL13 and CCL21, markers of immune infiltration of glandular tissues were further downregulated at 50 mg and 300 mg doses (Figure 1b). Similarly, a trend for downregulation of IFNPS was seen at the higher doses. Finally, some of these changes were also observed in corresponding salivary protein levels.
Conclusion:
No differential proteomic signatures related to baseline disease activity were identified. The dose response in clinical efficacy of ianalumab in patients with SjD observed in the phase 2b trial is also reflected at the protein level, with dose-dependent increase in depth and breadth of proteomic changes in serum by week 24. Confirmatory phase 3 trials are currently ongoing (NCT05349214 and NCT05350072).
REFERENCES:
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Acknowledgements:
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Disclosure of Interests:
Gwenny M. Verstappen Consultant for Argenx and Aurinia, Hendrika Bootsma Speaker for Novartis and Bristol-Myers Squibb, Consultant for Novartis, Bristol-Myers Squibb, Argenx, Roche, Union Chimique Belge, Unrestricted grant/research support from: Bristol-Myers Squibb, AstraZeneca, Novartis, Stephanie Finzel sponsored talks/courses: Abbvie, Chugai, Galapagos, Novartis, UCB, Consultant of: AstraZeneca, Novartis. Participated in Data Safety Monitoring Board or advisory board of AstraZeneca, Novartis, Received support for attending meetings and/or travel from Janssen, Andrea Grioni Shareholder of: Novartis, Employee of: Novartis, Benjamin Fisher Consultant of: Novartis, Roche, BMS, Galapagos, Janssen, Servier, UCB and Sanofi, Grant/research support from: Janssen, Celgene, Galapagos, Servier, Athena Papas Consultant of: Novartis, Grant/research support from: Novartis, Viela Bio, Exosome Dx, Celine Rauld Shareholder of: Novartis, Employee of: Novartis, Alexandre Avrameas Shareholder of: Novartis, Employee of: Novartis, Danny Tuckwell Employee of: Novartis, Jonas Zierer Shareholder of: Novartis, Employee of: Novartis, Valeria De Luca Shareholder of: Novartis, Employee of: Novartis, Enrico Ferrero Shareholder of: Novartis, Employee of: Novartis, Claire Bonal Shareholder of: Novartis, Employee of: Novartis, Andre da Costa Shareholder of: Novartis, Employee of: Novartis, Rainer Hillenbrand Shareholder of: Novartis, Employee of: Novartis, Isabelle Isnardi Shareholder of: Novartis, Employee of: Novartis, Wolfgang Hueber Shareholder of: Novartis, Employee of: Novartis.
Narcolepsy is a severe sleep disorder with characteristics of fatigue, fragmented sleep, cataplexy and hypnagogic hallucinations. Earlier clinical studies have reported the onset of schizophrenia after narcolepsy but the causality behind narcolepsy and schizophrenia is unknown. Our goal was to understand the causality between narcolepsy and schizophrenia. To estimate the comorbidity between narcolepsy and schizophrenia, we employed data from the FinRegistry that contains data for the total population of Finland in total 7.2 million individuals (N = 1664 individuals with narcolepsy and 55,372 with schizophrenia). We then used Mendelian randomization and previously published genome-wide association data to test the causality between narcolepsy and schizophrenia. We observed a robust causal association from narcolepsy to schizophrenia using the HLA-independent lead variants (P-value = 6.0 × 10−4), which was accentuated when including the HLA locus (P-value = 4.48 × 10−7). Furthermore, we observed a modest bidirectional causality from schizophrenia to narcolepsy (P-value = 0.015). There was no evidence of pleiotropy. Our findings indicate a causal relationship where narcolepsy may increase the risk for schizophrenia, and a bidirectional causality from schizophrenia to narcolepsy. Additionally, our results clarify the psychiatric burden in narcolepsy.
Although association studies have unveiled numerous correlations of biochemical markers with age and age-related diseases, we still lack an understanding of their mutual dependencies. To find molecular pathways that underlie age-related diseases as well as their comorbidities, we integrated aging markers from four different high-throughput omics datasets, namely epigenomics, transcriptomics, glycomics and metabolomics, with a comprehensive set of disease phenotypes from 510 participants of the TwinsUK cohort. We used graphical random forests to assess conditional dependencies between omics markers and phenotypes while eliminating mediated associations. Applying this novel approach for multi-omics data integration yields a model consisting of seven modules that represent distinct aspects of aging. These modules are connected by hubs that potentially trigger comorbidities of age-related diseases. As an example, we identified urate as one of these key players mediating the comorbidity of renal disease with body composition and obesity. Body composition variables are in turn associated with inflammatory IgG markers, mediated by the expression of the hormone oxytocin. Thus, oxytocin potentially contributes to the development of chronic low-grade inflammation, which often accompanies obesity. Our multi-omics graphical model demonstrates the interconnectivity of age-related diseases and highlights molecular markers of the aging process that might drive disease comorbidities.
ABSTRACT Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism 1–7 . This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases 8–11 . Here we present a genome-wide association study of 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 predominantly population-based cohorts. We discover over 400 independent loci and assign likely causal genes at two-thirds of these using detailed manual curation of highly plausible biological candidates. We highlight the importance of sample- and participant characteristics, such as fasting status and sample type, that can have significant impact on genetic associations, revealing direct and indirect associations on glucose and phenylalanine. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing for the first time the metabolic associations of an understudied phenotype, intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetoacetate and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.
One measure of protein glycosylation (GlycA) has been reported to predict higher cardiovascular risk by reflecting inflammatory pathways.The main objective of this study is to assess the role of a comprehensive panel of IgG glycosylation traits on traditional risk factors for cardiovascular disease and on presence of subclinical atherosclerosis in addition to GlycA.We measured 76 IgG glycosylation traits in 2970 women (age range, 40-79 years) from the TwinsUK cohort and correlated it to their estimated 10-year atherosclerotic cardiovascular disease risk score and their carotid and femoral plaque measured by ultrasound imaging. Eight IgG glycan traits are associated with the 10-year atherosclerotic cardiovascular disease risk score after adjusting for multiple tests and for individual risk factors-5 with increased risk and 3 with decreased risk. These glycans replicated in 967 women from ORCADES cohort (Orkney Complex Disease Study), and 6 of them were also associated in 845 men. A linear combination of IgG glycans and GlycA is also associated with presence of carotid (odds ratio, 1.55; 95% confidence interval, 1.25-1.93; P=7.5×10-5) and femoral (odds ratio, 1.32; 95% confidence interval, 1.06-1.64; P=0.01) plaque in a subset of women with atherosclerosis data after adjustment for traditional risk factors. One specific glycosylation trait, GP18-the percentage of FA2BG2S1 glycan in total IgG glycans, was negatively correlated with very-low-density lipoprotein and triglyceride levels in serum and with presence of carotid plaque (odds ratio, 0.60; 95% confidence interval, 0.50-0.71; P=5×10-4).We find molecular pathways linking IgG to arterial lesion formation. Glycosylation traits are independently associated with subclinical atherosclerosis. One specific trait related to the sialylated N-glycan is negatively correlated with cardiovascular disease risk, very-low-density lipoprotein and triglyceride serum levels, and presence of carotid plaque.
Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa.The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples.Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations.We combine gut microbiota profiles from 2764 British, 1023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them.Comparing populations we find that community structure is significantly more similar between datasets than expected by chance.Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations.This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.
Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa.The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples.Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations.We combine gut microbiota profiles from 2764 British, 1023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them.Comparing populations we find that community structure is significantly more similar between datasets than expected by chance.Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations.This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.
Abstract Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8–4.6] up to 6.2 [4.6–7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.
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