Skip to main content
Top
Published in: Inflammation 1/2024

08-09-2023 | Septicemia | RESEARCH

NMI Functions as Immuno-regulatory Molecule in Sepsis by Regulating Multiple Signaling Pathways

Authors: Jinhua Zeng, Zixin Yang, Dan Xu, Jierong Song, Yingfang Liu, Jing Qin, Zhuangfeng Weng

Published in: Inflammation | Issue 1/2024

Login to get access

Abstract

Sepsis-induced tissue and organ damage is caused by an overactive inflammatory response, immune dysfunction, and coagulation dysfunction. Danger-associated molecular pattern (DAMP) molecules play a critical role in the excessive inflammation observed in sepsis. In our previous research, we identified NMI as a new type of DAMP molecule that promotes inflammation in sepsis by binding to toll-like receptor 4 (TLR4) on macrophage surfaces, activating the NF-κB pathway, and releasing pro-inflammatory cytokines. However, it is still unknown whether NMI plays a significant role in other pathways. Our analysis of bulk and single-cell transcriptome data from the GEO database revealed a significant increase in NMI expression in neutrophils and monocytes in sepsis patients. It is likely that NMI functions through multiple receptors in sepsis, including IFNAR1, IFNAR2, TNFR1, TLR3, TLR1, IL9R, IL10RB, and TLR4. Furthermore, the correlation between NMI expression and the activation of NF-κB, MAPK, and JAK pathways, as well as the up-regulation of their downstream pro-inflammatory factors, demonstrates that NMI may exacerbate the inflammatory response through these signaling pathways. Finally, we demonstrated that STAT1 phosphorylation was enhanced in RAW cells upon stimulation with NMI, supporting the activation of JAK signaling pathway by NMI. Collectively, these findings shed new light on the functional mechanism of NMI in sepsis.
Appendix
Available only for authorised users
Literature
1.
go back to reference Rudd, K.E., S.C. Johnson, K.M. Agesa, K.A. Shackelford, D. Tsoi, D.R. Kievlan, et al. 2020. Global, regional, and national sepsis incidence and mortality, 1990–2017: Analysis for the Global Burden of Disease Study. Lancet 395 (10219): 200–211.CrossRefPubMedPubMedCentral Rudd, K.E., S.C. Johnson, K.M. Agesa, K.A. Shackelford, D. Tsoi, D.R. Kievlan, et al. 2020. Global, regional, and national sepsis incidence and mortality, 1990–2017: Analysis for the Global Burden of Disease Study. Lancet 395 (10219): 200–211.CrossRefPubMedPubMedCentral
2.
go back to reference Stanski, N.L., and H.R. Wong. 2020. Prognostic and predictive enrichment in sepsis. Nature Reviews. Nephrology 16 (1): 20–31.CrossRefPubMed Stanski, N.L., and H.R. Wong. 2020. Prognostic and predictive enrichment in sepsis. Nature Reviews. Nephrology 16 (1): 20–31.CrossRefPubMed
3.
4.
go back to reference Ackerman, M.H., T. Ahrens, J. Kelly, and A. Pontillo. 2021. Sepsis. Critical Care Nursing Clinics of North America 33 (4): 407–418.CrossRefPubMed Ackerman, M.H., T. Ahrens, J. Kelly, and A. Pontillo. 2021. Sepsis. Critical Care Nursing Clinics of North America 33 (4): 407–418.CrossRefPubMed
5.
6.
go back to reference Rubartelli, A., and M.T. Lotze. 2007. Inside, outside, upside down: Damage-associated molecular-pattern molecules (DAMPs) and redox. Trends in Immunology 28 (10): 429–436.CrossRefPubMed Rubartelli, A., and M.T. Lotze. 2007. Inside, outside, upside down: Damage-associated molecular-pattern molecules (DAMPs) and redox. Trends in Immunology 28 (10): 429–436.CrossRefPubMed
8.
go back to reference Takeuchi, O., and S. Akira. 2010. Pattern recognition receptors and inflammation. Cell 140 (6): 805–820.CrossRefPubMed Takeuchi, O., and S. Akira. 2010. Pattern recognition receptors and inflammation. Cell 140 (6): 805–820.CrossRefPubMed
9.
go back to reference Singer, M., C.S. Deutschman, C.W. Seymour, M. Shankar-Hari, D. Annane, M. Bauer, et al. 2016. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315 (8): 801–810.CrossRefPubMedPubMedCentral Singer, M., C.S. Deutschman, C.W. Seymour, M. Shankar-Hari, D. Annane, M. Bauer, et al. 2016. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315 (8): 801–810.CrossRefPubMedPubMedCentral
10.
go back to reference Salomao, R., B.L. Ferreira, M.C. Salomao, S.S. Santos, L.C.P. Azevedo, and M.K.C. Brunialti. 2019. Sepsis: Evolving concepts and challenges. Brazilian Journal of Medical and Biological Research 52 (4): e8595.CrossRefPubMedPubMedCentral Salomao, R., B.L. Ferreira, M.C. Salomao, S.S. Santos, L.C.P. Azevedo, and M.K.C. Brunialti. 2019. Sepsis: Evolving concepts and challenges. Brazilian Journal of Medical and Biological Research 52 (4): e8595.CrossRefPubMedPubMedCentral
11.
go back to reference Aziz, M., A. Jacob, W.L. Yang, A. Matsuda, and P. Wang. 2013. Current trends in inflammatory and immunomodulatory mediators in sepsis. Journal of Leukocyte Biology 93 (3): 329–342.CrossRefPubMedPubMedCentral Aziz, M., A. Jacob, W.L. Yang, A. Matsuda, and P. Wang. 2013. Current trends in inflammatory and immunomodulatory mediators in sepsis. Journal of Leukocyte Biology 93 (3): 329–342.CrossRefPubMedPubMedCentral
12.
go back to reference Sunden-Cullberg, J., A. Norrby-Teglund, A. Rouhiainen, H. Rauvala, G. Herman, K.J. Tracey, et al. 2005. Persistent elevation of high mobility group box-1 protein (HMGB1) in patients with severe sepsis and septic shock. Critical Care Medicine 33 (3): 564–573.CrossRefPubMed Sunden-Cullberg, J., A. Norrby-Teglund, A. Rouhiainen, H. Rauvala, G. Herman, K.J. Tracey, et al. 2005. Persistent elevation of high mobility group box-1 protein (HMGB1) in patients with severe sepsis and septic shock. Critical Care Medicine 33 (3): 564–573.CrossRefPubMed
13.
go back to reference Zhang, Q., M. Raoof, Y. Chen, Y. Sumi, T. Sursal, W. Junger, et al. 2010. Circulating mitochondrial DAMPs cause inflammatory responses to injury. Nature 464 (7285): 104–107.CrossRefPubMedPubMedCentral Zhang, Q., M. Raoof, Y. Chen, Y. Sumi, T. Sursal, W. Junger, et al. 2010. Circulating mitochondrial DAMPs cause inflammatory responses to injury. Nature 464 (7285): 104–107.CrossRefPubMedPubMedCentral
14.
go back to reference Qiang, X., W.L. Yang, R. Wu, M. Zhou, A. Jacob, W. Dong, et al. 2013. Cold-inducible RNA-binding protein (CIRP) triggers inflammatory responses in hemorrhagic shock and sepsis. Nature Medicine 19 (11): 1489–1495.CrossRefPubMedPubMedCentral Qiang, X., W.L. Yang, R. Wu, M. Zhou, A. Jacob, W. Dong, et al. 2013. Cold-inducible RNA-binding protein (CIRP) triggers inflammatory responses in hemorrhagic shock and sepsis. Nature Medicine 19 (11): 1489–1495.CrossRefPubMedPubMedCentral
15.
go back to reference Ekaney, M.L., G.P. Otto, M. Sossdorf, C. Sponholz, M. Boehringer, W. Loesche, et al. 2014. Impact of plasma histones in human sepsis and their contribution to cellular injury and inflammation. Critical Care 18 (5). Ekaney, M.L., G.P. Otto, M. Sossdorf, C. Sponholz, M. Boehringer, W. Loesche, et al. 2014. Impact of plasma histones in human sepsis and their contribution to cellular injury and inflammation. Critical Care 18 (5).
16.
go back to reference Denstaedt, S.J., J.L. Spencer-Segal, M.W. Newstead, K. Laborc, A.P. Zhao, A. Hjelmaas, et al. 2018. S100A8/A9 drives neuroinflammatory priming and protects against anxiety-like behavior after sepsis. The Journal of Immunology 200 (9): 3188–3200.CrossRefPubMed Denstaedt, S.J., J.L. Spencer-Segal, M.W. Newstead, K. Laborc, A.P. Zhao, A. Hjelmaas, et al. 2018. S100A8/A9 drives neuroinflammatory priming and protects against anxiety-like behavior after sepsis. The Journal of Immunology 200 (9): 3188–3200.CrossRefPubMed
17.
go back to reference Vulczak, A., C.H.R. Catalao, L.A.P. Freitas, M.J.A. Rocha, 2019. HSP-target of therapeutic agents in sepsis treatment. International Journal of Molecular Science 20 (17). Vulczak, A., C.H.R. Catalao, L.A.P. Freitas, M.J.A. Rocha, 2019. HSP-target of therapeutic agents in sepsis treatment. International Journal of Molecular Science 20 (17).
18.
go back to reference Nascimento, D.C., P.H. Melo, A.R. Pineros, R.G. Ferreira, D.F. Colon, P.B. Donate, et al. 2017. IL-33 contributes to sepsis-induced long-term immunosuppression by expanding the regulatory T cell population. Nature Communications 8: 14919.CrossRefPubMedPubMedCentral Nascimento, D.C., P.H. Melo, A.R. Pineros, R.G. Ferreira, D.F. Colon, P.B. Donate, et al. 2017. IL-33 contributes to sepsis-induced long-term immunosuppression by expanding the regulatory T cell population. Nature Communications 8: 14919.CrossRefPubMedPubMedCentral
19.
go back to reference Mouncey, P.R., T.M. Osborn, G.S. Power, D.A. Harrison, M.Z. Sadique, R.D. Grieve, et al. 2015. Trial of early, goal-directed resuscitation for septic shock. New England Journal of Medicine 372 (14): 1301–1311.CrossRefPubMed Mouncey, P.R., T.M. Osborn, G.S. Power, D.A. Harrison, M.Z. Sadique, R.D. Grieve, et al. 2015. Trial of early, goal-directed resuscitation for septic shock. New England Journal of Medicine 372 (14): 1301–1311.CrossRefPubMed
20.
go back to reference Zhou, M., M. Aziz, and P. Wang. 2021. Damage-associated molecular patterns as double-edged swords in sepsis. Antioxidants & Redox Signaling 35 (15): 1308–1323.CrossRef Zhou, M., M. Aziz, and P. Wang. 2021. Damage-associated molecular patterns as double-edged swords in sepsis. Antioxidants & Redox Signaling 35 (15): 1308–1323.CrossRef
21.
go back to reference Denning, N.L., M. Aziz, A. Murao, S.D. Gurien, M. Ochani, J.M. Prince, et al. 2020. Extracellular CIRP as an endogenous TREM-1 ligand to fuel inflammation in sepsis. JCI Insight 5 (5). Denning, N.L., M. Aziz, A. Murao, S.D. Gurien, M. Ochani, J.M. Prince, et al. 2020. Extracellular CIRP as an endogenous TREM-1 ligand to fuel inflammation in sepsis. JCI Insight 5 (5).
22.
go back to reference Xiahou, Z., X. Wang, J. Shen, X. Zhu, F. Xu, R. Hu, et al. 2017. NMI and IFP35 serve as proinflammatory DAMPs during cellular infection and injury. Nature Communications 8 (1): 950.CrossRefPubMedPubMedCentral Xiahou, Z., X. Wang, J. Shen, X. Zhu, F. Xu, R. Hu, et al. 2017. NMI and IFP35 serve as proinflammatory DAMPs during cellular infection and injury. Nature Communications 8 (1): 950.CrossRefPubMedPubMedCentral
23.
go back to reference Jing, X., Y. Yao, D. Wu, H. Hong, X. Feng, N. Xu, et al. 2021. IFP35 family proteins promote neuroinflammation and multiple sclerosis. Proceedings National Academy of Sciences USA 118 (32). Jing, X., Y. Yao, D. Wu, H. Hong, X. Feng, N. Xu, et al. 2021. IFP35 family proteins promote neuroinflammation and multiple sclerosis. Proceedings National Academy of Sciences USA 118 (32).
24.
go back to reference Bosmann, M., and P.A. Ward. 2013. The inflammatory response in sepsis. Trends in Immunology 34 (3): 129–136.CrossRefPubMed Bosmann, M., and P.A. Ward. 2013. The inflammatory response in sepsis. Trends in Immunology 34 (3): 129–136.CrossRefPubMed
25.
go back to reference Schaefer, L. 2014. Complexity of danger: The diverse nature of damage-associated molecular patterns. Journal of Biological Chemistry 289 (51): 35237–35245.CrossRefPubMedPubMedCentral Schaefer, L. 2014. Complexity of danger: The diverse nature of damage-associated molecular patterns. Journal of Biological Chemistry 289 (51): 35237–35245.CrossRefPubMedPubMedCentral
26.
go back to reference Song, B., X. Luo, X. Luo, Y. Liu, Z. Niu, X. Zeng, 2022. Learning spatial structures of proteins improves protein-protein interaction prediction. Brief Bioinformatics 23 (2). Song, B., X. Luo, X. Luo, Y. Liu, Z. Niu, X. Zeng, 2022. Learning spatial structures of proteins improves protein-protein interaction prediction. Brief Bioinformatics 23 (2).
27.
go back to reference Sledzieski, S., R. Singh, L. Cowen, B. Berger. 2021. D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions. Cell Systems 12 (10):969–82 e6. Sledzieski, S., R. Singh, L. Cowen, B. Berger. 2021. D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions. Cell Systems 12 (10):969–82 e6.
28.
go back to reference Zhang, Q.C., D. Petrey, L. Deng, L. Qiang, Y. Shi, C.A. Thu, et al. 2012. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490 (7421): 556–560.CrossRefPubMedPubMedCentral Zhang, Q.C., D. Petrey, L. Deng, L. Qiang, Y. Shi, C.A. Thu, et al. 2012. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490 (7421): 556–560.CrossRefPubMedPubMedCentral
30.
go back to reference Jin, S., C.F. Guerrero-Juarez, L. Zhang, I. Chang, R. Ramos, C.H. Kuan, et al. 2021. Inference and analysis of cell-cell communication using Cell Chat. Nature Communications 12 (1): 1088.CrossRefPubMedPubMedCentral Jin, S., C.F. Guerrero-Juarez, L. Zhang, I. Chang, R. Ramos, C.H. Kuan, et al. 2021. Inference and analysis of cell-cell communication using Cell Chat. Nature Communications 12 (1): 1088.CrossRefPubMedPubMedCentral
31.
go back to reference Janeway, C.A., Jr. 1989. Approaching the asymptote? Evolution and revolution in immunology. Cold Spring Harbor Symposia on Quantitative Biology 54 (Pt 1): 1–13.CrossRefPubMed Janeway, C.A., Jr. 1989. Approaching the asymptote? Evolution and revolution in immunology. Cold Spring Harbor Symposia on Quantitative Biology 54 (Pt 1): 1–13.CrossRefPubMed
32.
go back to reference Ye, W., X. Liu, Y. Bai, N. Tang, G. Wu, X. Wang, et al. 2021. Sepsis activates the TLR4/MyD88 pathway in Schwann cells to promote infiltration of macrophages, thereby impeding neuromuscular function. Shock 55 (1): 90–99.CrossRefPubMed Ye, W., X. Liu, Y. Bai, N. Tang, G. Wu, X. Wang, et al. 2021. Sepsis activates the TLR4/MyD88 pathway in Schwann cells to promote infiltration of macrophages, thereby impeding neuromuscular function. Shock 55 (1): 90–99.CrossRefPubMed
33.
go back to reference Sharma, A., K. Kontodimas, and M. Bosmann. 2021. The MAVS Immune recognition pathway in viral infection and sepsis. Antioxidants & Redox Signaling 35 (16): 1376–1392.CrossRef Sharma, A., K. Kontodimas, and M. Bosmann. 2021. The MAVS Immune recognition pathway in viral infection and sepsis. Antioxidants & Redox Signaling 35 (16): 1376–1392.CrossRef
34.
go back to reference Kolaczkowska, E., and P. Kubes. 2013. Neutrophil recruitment and function in health and inflammation. Nature Reviews Immunology 13 (3): 159–175.CrossRefPubMed Kolaczkowska, E., and P. Kubes. 2013. Neutrophil recruitment and function in health and inflammation. Nature Reviews Immunology 13 (3): 159–175.CrossRefPubMed
35.
go back to reference Vogel, S., R. Bodenstein, Q.W. Chen, S. Feil, R. Feil, J. Rheinlaender, et al. 2015. Platelet-derived HMGB1 is a critical mediator of thrombosis. The Journal of Clinical Investigation 125 (12): 4638–4654.CrossRefPubMedPubMedCentral Vogel, S., R. Bodenstein, Q.W. Chen, S. Feil, R. Feil, J. Rheinlaender, et al. 2015. Platelet-derived HMGB1 is a critical mediator of thrombosis. The Journal of Clinical Investigation 125 (12): 4638–4654.CrossRefPubMedPubMedCentral
36.
go back to reference Reyes, M., M.R. Filbin, R.P. Bhattacharyya, K. Billman, T. Eisenhaure, D.T. Hung, et al. 2020. An immune-cell signature of bacterial sepsis. Nature Medicine 26 (3): 333–340.CrossRefPubMedPubMedCentral Reyes, M., M.R. Filbin, R.P. Bhattacharyya, K. Billman, T. Eisenhaure, D.T. Hung, et al. 2020. An immune-cell signature of bacterial sepsis. Nature Medicine 26 (3): 333–340.CrossRefPubMedPubMedCentral
37.
go back to reference Clere-Jehl, R., A. Mariotte, F. Meziani, S. Bahram, P. Georgel, and J. Helms. 2020. JAK-STAT targeting offers novel therapeutic opportunities in sepsis. Trends in Molecular Medicine 26 (11): 987–1002.CrossRefPubMed Clere-Jehl, R., A. Mariotte, F. Meziani, S. Bahram, P. Georgel, and J. Helms. 2020. JAK-STAT targeting offers novel therapeutic opportunities in sepsis. Trends in Molecular Medicine 26 (11): 987–1002.CrossRefPubMed
38.
go back to reference Tabone, O., M. Mommert, C. Jourdan, E. Cerrato, M. Legrand, A. Lepape, et al. 2018. Endogenous retroviruses transcriptional modulation after severe infection, trauma and burn. Frontiers in Immunology 9: 3091.CrossRefPubMed Tabone, O., M. Mommert, C. Jourdan, E. Cerrato, M. Legrand, A. Lepape, et al. 2018. Endogenous retroviruses transcriptional modulation after severe infection, trauma and burn. Frontiers in Immunology 9: 3091.CrossRefPubMed
39.
go back to reference Ouyang, W., and A. O’Garra. 2019. IL-10 Family Cytokines IL-10 and IL-22: From Basic Science to Clinical Translation. Immunity 50 (4): 871–891.CrossRefPubMed Ouyang, W., and A. O’Garra. 2019. IL-10 Family Cytokines IL-10 and IL-22: From Basic Science to Clinical Translation. Immunity 50 (4): 871–891.CrossRefPubMed
40.
go back to reference Shen, W., Z. Song, X. Zhong, M. Huang, D. Shen, P. Gao, et al. 2022. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. iMeta 1 (3):e36. Shen, W., Z. Song, X. Zhong, M. Huang, D. Shen, P. Gao, et al. 2022. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. iMeta 1 (3):e36.
41.
42.
go back to reference Satija, R., J.A. Farrell, D. Gennert, A.F. Schier, and A. Regev. 2015. Spatial reconstruction of single-cell gene expression data. Nature Biotechnology. 33 (5): 495–502.CrossRefPubMedPubMedCentral Satija, R., J.A. Farrell, D. Gennert, A.F. Schier, and A. Regev. 2015. Spatial reconstruction of single-cell gene expression data. Nature Biotechnology. 33 (5): 495–502.CrossRefPubMedPubMedCentral
43.
go back to reference Korsunsky, I., N. Millard, J. Fan, K. Slowikowski, F. Zhang, K. Wei, et al. 2019. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods 16 (12): 1289–1296.CrossRefPubMedPubMedCentral Korsunsky, I., N. Millard, J. Fan, K. Slowikowski, F. Zhang, K. Wei, et al. 2019. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods 16 (12): 1289–1296.CrossRefPubMedPubMedCentral
44.
go back to reference Becht, E., L. McInnes, J. Healy, C.A. Dutertre, I.W.H. Kwok, L.G. Ng, et al. 2018. Dimensionality reduction for visualizing single-cell data using UMAP. National Biotechnology. Becht, E., L. McInnes, J. Healy, C.A. Dutertre, I.W.H. Kwok, L.G. Ng, et al. 2018. Dimensionality reduction for visualizing single-cell data using UMAP. National Biotechnology.
45.
go back to reference Clarke, Z.A., T.S. Andrews, J. Atif, D. Pouyabahar, B.T. Innes, S.A. MacParland, et al. 2021. Tutorial: Guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nature Protocols 16 (6): 2749–2764.CrossRefPubMed Clarke, Z.A., T.S. Andrews, J. Atif, D. Pouyabahar, B.T. Innes, S.A. MacParland, et al. 2021. Tutorial: Guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nature Protocols 16 (6): 2749–2764.CrossRefPubMed
46.
go back to reference Aran, D., A.P. Looney, L. Liu, E. Wu, V. Fong, A. Hsu, et al. 2019. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology 20 (2): 163–172.CrossRefPubMedPubMedCentral Aran, D., A.P. Looney, L. Liu, E. Wu, V. Fong, A. Hsu, et al. 2019. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology 20 (2): 163–172.CrossRefPubMedPubMedCentral
47.
go back to reference Han, X., Z. Zhou, L. Fei, H. Sun, R. Wang, Y. Chen, et al. 2020. Construction of a human cell landscape at single-cell level. Nature 581 (7808): 303–309.CrossRefPubMed Han, X., Z. Zhou, L. Fei, H. Sun, R. Wang, Y. Chen, et al. 2020. Construction of a human cell landscape at single-cell level. Nature 581 (7808): 303–309.CrossRefPubMed
48.
go back to reference Singh, R., K. Devkota, S. Sledzieski, B. Berger, and L. Cowen. 2022. Topsy-Turvy: Integrating a global view into sequence-based PPI prediction. Bioinformatics (Oxford, England). 38 (Suppl 1): i264–i272.PubMedPubMedCentral Singh, R., K. Devkota, S. Sledzieski, B. Berger, and L. Cowen. 2022. Topsy-Turvy: Integrating a global view into sequence-based PPI prediction. Bioinformatics (Oxford, England). 38 (Suppl 1): i264–i272.PubMedPubMedCentral
Metadata
Title
NMI Functions as Immuno-regulatory Molecule in Sepsis by Regulating Multiple Signaling Pathways
Authors
Jinhua Zeng
Zixin Yang
Dan Xu
Jierong Song
Yingfang Liu
Jing Qin
Zhuangfeng Weng
Publication date
08-09-2023
Publisher
Springer US
Published in
Inflammation / Issue 1/2024
Print ISSN: 0360-3997
Electronic ISSN: 1573-2576
DOI
https://doi.org/10.1007/s10753-023-01893-4

Other articles of this Issue 1/2024

Inflammation 1/2024 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine