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Wang L, Guo X, Shi H, Ma Y, Bao H, Jiang L, Zhao L, Feng Z, Zhu T, Lu L. CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction. BMC Med Inform Decis Mak 2025; 25:165. [PMID: 40234903 PMCID: PMC12001402 DOI: 10.1186/s12911-025-02981-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2024] [Accepted: 03/19/2025] [Indexed: 04/17/2025] Open
Abstract
BACKGROUND Mortality prediction is critical in clinical care, particularly in intensive care units (ICUs), where early identification of high-risk patients can inform treatment decisions. While deep learning (DL) models have demonstrated significant potential in this task, most suffer from limited generalizability, which hinders their widespread clinical application. Additionally, the class imbalance in electronic health records (EHRs) complicates model training. This study aims to develop a causally-informed prediction model that incorporates underlying causal relationships to mitigate class imbalance, enabling more stable mortality predictions. METHODS This study introduces the CRISP model (Causal Relationship Informed Superior Prediction), which leverages native counterfactuals to augment the minority class and constructs patient representations by incorporating causal structures to enhance mortality prediction. Patient data were obtained from the public MIMIC-III and MIMIC-IV databases, as well as an additional dataset from the West China Hospital of Sichuan University (WCHSU). RESULTS A total of 69,190 ICU cases were included, with 30,844 cases from MIMIC-III, 27,362 cases from MIMIC-IV, and 10,984 cases from WCHSU. The CRISP model demonstrated stable performance in mortality prediction across the 3 datasets, achieving AUROC (0.9042-0.9480) and AUPRC (0.4771-0.7611). CRISP's data augmentation module showed predictive performance comparable to commonly used interpolation-based oversampling techniques. CONCLUSION CRISP achieves better generalizability across different patient groups, compared to various baseline algorithms, thereby enhancing the practical application of DL in clinical decision support. TRIAL REGISTRATION Trial registration information for the WCHSU data is available on the Chinese Clinical Trial Registry website ( http://www.chictr.org.cn ), with the registration number ChiCTR1900025160. The recruitment period for the data was from August 5, 2019, to August 31, 2021.
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Affiliation(s)
- Linna Wang
- College of Computer Science, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, Sichuan, 610065, China
| | - Xinyu Guo
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Haoyue Shi
- College of Mechanical Engineering, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, Sichuan, 610065, China
| | - Yuehang Ma
- College of Computer Science, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, Sichuan, 610065, China
| | - Han Bao
- College of Computer Science, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, Sichuan, 610065, China
| | - Lihua Jiang
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Li Zhao
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Ziliang Feng
- College of Computer Science, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, Sichuan, 610065, China
| | - Tao Zhu
- Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Li Lu
- College of Computer Science, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, Sichuan, 610065, China.
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Maheshwari S, Singh A. Dendrimers: Patents for Alzheimer's Disease. RECENT PATENTS ON NANOTECHNOLOGY 2025; 19:356-363. [PMID: 37904560 DOI: 10.2174/1872210517666230831154408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/06/2023] [Accepted: 07/18/2023] [Indexed: 11/01/2023]
Abstract
Cells and nervous system connections that are crucial for movement, coordination, strength, sensation, and thought are gradually damaged in neurodegenerative illnesses. Amyloid beta (Aβ)- accumulating macromolecules in the brain are the primary cause of the disease's chronic symptoms, according to analysis carried out during the last 20 years. Plaques and clumps of amyloid- build up in the brain, obstructing neuronal signals and destroying neural connections. Tau, a protein that results in the formation of "neurofibrillary tangles" in the brain, another hallmark of neuronal death, has been the focus of a lot of research. Dendrimers Delivery (DDs) is one of the most promising advancements in nanotechnology for biomedical applications, particularly drug delivery. Some of the main categories of dendrimers employed in the successful management of neurodegenerative illnesses are polyamidoamine dendrimers (PAMAM) dendrimers, polypropylenimine dendrimers (PPI), Poly-l-lysine dendrimers (PLL), and carbosilane dendrimers. The tight blood-brain barrier (BBB), which limits the entry of medications or therapeutic agents, makes it difficult to treat central nervous system disorders. Dendrimers have attracted the attention of scientists more than other non-invasive methods of drug delivery across the BBB and improve the uptake of medicines in the brain's target tissues. The major benefits of dendrimers include their adaptability, biocompatibility, ability to load pharmaceuticals into the core and surface, and nanosize. The patents provide "composition of matter" protection for Starpharma's dendrimer technologies for drug delivery out to 2029 in the United States, which is the world's largest pharmaceutical market for several important drug classes. This review has updated the status of the patent and clinical trials literature pertaining to dendrimer use in AD.
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Affiliation(s)
- Shubhrat Maheshwari
- Faculty of Pharmaceutical Sciences, Rama University, Mandhana, Bithoor Road, Kanpur, Uttar Pradesh, 209217, India
| | - Aditya Singh
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, Uttar Pradesh, 226026, India
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Stolfi F, Abreu H, Sinella R, Nembrini S, Centonze S, Landra V, Brasso C, Cappellano G, Rocca P, Chiocchetti A. Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine. Front Psychiatry 2024; 15:1422939. [PMID: 38938457 PMCID: PMC11210496 DOI: 10.3389/fpsyt.2024.1422939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 05/28/2024] [Indexed: 06/29/2024] Open
Abstract
Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.
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Affiliation(s)
- Fabiola Stolfi
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Hugo Abreu
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Riccardo Sinella
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Nembrini
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Centonze
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Virginia Landra
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Giuseppe Cappellano
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Paola Rocca
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Annalisa Chiocchetti
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
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Qiu C, Su K, Luo Z, Tian Q, Zhao L, Wu L, Deng H, Shen H. Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction. Front Artif Intell 2024; 7:1355287. [PMID: 38919268 PMCID: PMC11196804 DOI: 10.3389/frai.2024.1355287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset. Conclusion In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.
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Affiliation(s)
| | | | | | | | | | | | - Hongwen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States
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Jones DP. Redox organization of living systems. Free Radic Biol Med 2024; 217:179-189. [PMID: 38490457 PMCID: PMC11313653 DOI: 10.1016/j.freeradbiomed.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/08/2024] [Accepted: 03/13/2024] [Indexed: 03/17/2024]
Abstract
Redox organization governs an underlying simplicity in living systems. Critically, redox reactions enable the essential characteristics of life: extraction of energy from the environment, use of energy to support metabolic and structural organization, use of dynamic redox responses to defend against environmental threats, and use of redox mechanisms to direct differentiation of cells and organ systems essential for reproduction. These processes are sustained through a redox context in which electron donor/acceptor couples are poised at substantially different steady-state redox potentials, some with relatively reducing steady states and others with relatively oxidizing steady states. Redox-sensitive thiols of the redox proteome, as well as low molecular weight redox-active molecules, are maintained individually by the kinetics of oxidation-reduction within this redox system. Recent research has revealed opposing network interactions of the metallome, redox proteome, metabolome and transcriptome, which appear to be an evolved redox response structure to maintain stability of an organism in the presence of variable oxidative environments. Considerable opportunity exists to improve human health through detailed understanding of these redox networks so that targeted interventions can be developed to support new avenues for redox medicine.
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Affiliation(s)
- Dean P Jones
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, Emory University, Whitehead Biomedical Research Building, 615 Michael St, RM205P, Atlanta, GA, 30322, USA.
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Matos DM. CD5-negative chronic lymphocytic leukemia: Does this entity really exist? CYTOMETRY. PART B, CLINICAL CYTOMETRY 2024; 106:126-137. [PMID: 38017706 DOI: 10.1002/cyto.b.22151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/30/2023]
Affiliation(s)
- Daniel Mazza Matos
- Flow Cytometry Section, Cell Processing Center (CPC), Center of Hematology and Hemotherapy of Ceará (HEMOCE), Fortaleza, Brazil
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Singh A, Ansari VA, Mahmood T, Ahsan F, Maheshwari S. Repercussion of Primary Nucleation Pathway: Dementia and Cognitive Impairment. Curr Aging Sci 2024; 17:196-204. [PMID: 38083895 DOI: 10.2174/0118746098243327231117113748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 07/05/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2024]
Abstract
Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and prion disease, are characterized by the conversion of normally soluble proteins or peptides into aggregated amyloidal fibrils. These diseases result in the permanent loss of specific types of neurons, making them incurable and devastating. Research on animal models of memory problems mentioned in this article contributes to our knowledge of brain health and functionality. Neurodegenerative disorders, which often lead to cognitive impairment and dementia, are becoming more prevalent as global life expectancy increases. These diseases cause severe neurological impairment and neuronal death, making them highly debilitating. Exploring and understanding these complex diseases offer significant insights into the fundamental processes essential for maintaining brain health. Exploring the intricate mechanisms underlying neurodegenerative diseases not only holds promise for potential treatments but also enhances our understanding of fundamental brain health and functionality. By unraveling the complexities of these disorders, researchers can pave the way for advancements in diagnosis, treatment, and ultimately, improving the lives of individuals affected by neurodegenerative diseases.
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Affiliation(s)
- Aditya Singh
- Faculty of Pharmacy, Integral University, Lucknow, 226026, India
| | - Vaseem A Ansari
- Faculty of Pharmacy, Integral University, Lucknow, 226026, India
| | - Tarique Mahmood
- Faculty of Pharmacy, Integral University, Lucknow, 226026, India
| | - Farogh Ahsan
- Faculty of Pharmacy, Integral University, Lucknow, 226026, India
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Weber E, Downward G, Pinho MGM, Van Vuuren DP. Healthy lives and well-being for all at all ages: expanding representations of determinants of health within systems dynamics and integrated assessment models. SUSTAINABLE EARTH 2023; 6:15. [PMID: 39807351 PMCID: PMC11728693 DOI: 10.1186/s42055-023-00064-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/01/2023] [Indexed: 01/16/2025]
Abstract
Integrated Assessment Models (IAMs) and System Dynamic Models (SDMs) are starting to incorporate representations of the impact of environmental changes on health and socio-economic development into their modelling frameworks. We use this brief review to provide an overview of how health and well-being are currently represented in IAMs and SDMs. A grey literature search on 12 selected model host websites and their corresponding Wiki pages was conducted. Model descriptions, coverage and publications were then tabulated. Additional potential determinants related to health were then suggested based on emerging environmental health literature. Based on these tabulations, it was determined that many individual health outcomes are not represented and thus not analyzed. Social well-being is not represented at all. Additionally, potentially health relevant determinants such as chemical or metal exposure and water pollution are rarely represented in models. Most models have representations of climate, outdoor air pollution and food availability. Air pollution was the most analyzed determinant, especially in relation to its respiratory effects. We suggest that future modelers incorporate more representations of environmental determinants influencing health, and to analyze all available determinants in relation to a wider array of health outcomes. Perhaps, if and when broader determinants of health are represented in IAMs and SDMs, then a composite of these determinants could be used to determine a population's ability to achieve elements that also contribute to social well-being. Supplementary Information The online version contains supplementary material available at 10.1186/s42055-023-00064-5.
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Affiliation(s)
- Eartha Weber
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands
| | - George Downward
- Department Population Health Sciences, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
| | - Maria G. M. Pinho
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands
| | - Detlef P. Van Vuuren
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands
- PBL Netherlands Environmental Assessment Agency, The Hague, The Netherlands
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Lu H, Uddin S. Embedding-based link predictions to explore latent comorbidity of chronic diseases. Health Inf Sci Syst 2023; 11:2. [PMID: 36593862 PMCID: PMC9803807 DOI: 10.1007/s13755-022-00206-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 12/13/2022] [Indexed: 12/31/2022] Open
Abstract
Purpose Comorbidity is a term used to describe when a patient simultaneously has more than one chronic disease. Comorbidity is a significant health issue that affects people worldwide. This study aims to use machine learning and graph theory to predict the comorbidity of chronic diseases. Methods A patient-disease bipartite graph is constructed based on the administrative claim data. The bipartite graph projection approach was used to create the comorbidity network. For the link prediction task, three graph machine learning embedding-based models (node2vec, graph neural networks and hand-crafted approach) with different variants were used on the comorbidity network to compare their performance. This study also considered three commonly used similarity-based link prediction approaches (Jaccard coefficient, Adamic-Adar index and Resource allocation index) for performance comparison. Results The results showed that the embedding-based hand-crafted features technique achieved outstanding performance compared with the remaining similarity-based and embedding-based models. Especially, the hand-crafted technique with the extreme gradient boosting classifier achieved the highest accuracy (91.67%), followed by the same technique with the Logistic regression classifier (90.26%). For this shallow embedding method, the Jaccard coefficient and the degree centrality of the original chronic disease were the most important features for comorbidity prediction. Conclusion The proposed framework can be used to predict the comorbidity of chronic disease at an early stage of hospital admission. Thus, the prediction outcome could be valuable for medical practice, giving healthcare providers more control over their services and lowering expenses.
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Affiliation(s)
- Haohui Lu
- School of Project Management, Faculty of Engineering, The University of Sydney, Level 2, 21 Ross Street, Forest Lodge, NSW 2037 Australia
| | - Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Level 2, 21 Ross Street, Forest Lodge, NSW 2037 Australia
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Morselli Gysi D, Barabási AL. Noncoding RNAs improve the predictive power of network medicine. Proc Natl Acad Sci U S A 2023; 120:e2301342120. [PMID: 37906646 PMCID: PMC10636370 DOI: 10.1073/pnas.2301342120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 09/09/2023] [Indexed: 11/02/2023] Open
Abstract
Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.
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Affiliation(s)
- Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
- Department of Network and Data Science, Central European University, Budapest1051, Hungary
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Fabbri LM, Celli BR, Agustí A, Criner GJ, Dransfield MT, Divo M, Krishnan JK, Lahousse L, Montes de Oca M, Salvi SS, Stolz D, Vanfleteren LEGW, Vogelmeier CF. COPD and multimorbidity: recognising and addressing a syndemic occurrence. THE LANCET. RESPIRATORY MEDICINE 2023; 11:1020-1034. [PMID: 37696283 DOI: 10.1016/s2213-2600(23)00261-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/21/2023] [Accepted: 06/30/2023] [Indexed: 09/13/2023]
Abstract
Most patients with chronic obstructive pulmonary disease (COPD) have at least one additional, clinically relevant chronic disease. Those with the most severe airflow obstruction will die from respiratory failure, but most patients with COPD die from non-respiratory disorders, particularly cardiovascular diseases and cancer. As many chronic diseases have shared risk factors (eg, ageing, smoking, pollution, inactivity, and poverty), we argue that a shift from the current paradigm in which COPD is considered as a single disease with comorbidities, to one in which COPD is considered as part of a multimorbid state-with co-occurring diseases potentially sharing pathobiological mechanisms-is needed to advance disease prevention, diagnosis, and management. The term syndemics is used to describe the co-occurrence of diseases with shared mechanisms and risk factors, a novel concept that we propose helps to explain the clustering of certain morbidities in patients diagnosed with COPD. A syndemics approach to understanding COPD could have important clinical implications, in which the complex disease presentations in these patients are addressed through proactive diagnosis, assessment of severity, and integrated management of the COPD multimorbid state, with a patient-centred rather than a single-disease approach.
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Affiliation(s)
- Leonardo M Fabbri
- Section of Respiratory Medicine, Department of Translational Medicine, University of Ferrara, Ferrara, Italy
| | - Bartolome R Celli
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alvar Agustí
- Cátedra Salud Respiratoria, Universitat de Barcelona, Barcelona, Spain; Institut Respiratori, Clínic Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédicas August Pi i Sunyer, Barcelona, Spain; Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Spain
| | - Gerard J Criner
- Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA
| | - Mark T Dransfield
- Lung Health Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Miguel Divo
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jamuna K Krishnan
- Division of Pulmonary and Critical Care, Weill Cornell Medicine, New York, NY, USA
| | - Lies Lahousse
- Department of Bioanalysis, Ghent University, Ghent, Belgium
| | - Maria Montes de Oca
- School of Medicine, Universidad Central de Venezuela, Caracas, Venezuela; Hospital Centro Medico de Caracas, Caracas, Venezuela
| | - Sundeep S Salvi
- Pulmocare Research and Education (PURE) Foundation, Pune, India; School of Health Sciences, Symbiosis International Deemed University, Pune, India
| | - Daiana Stolz
- Clinic of Respiratory Medicine and Pulmonary Cell Research, University Hospital Basel, Basel, Switzerland; Department of Clinical Research, University Hospital Basel, Basel, Switzerland; Clinic of Respiratory Medicine and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lowie E G W Vanfleteren
- COPD Center, Department of Respiratory Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Centre Giessen and Marburg, Philipps University of Marburg, Member of the German Centre for Lung Research, Marburg, Germany.
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Amati F, Spagnolo P, Ryerson CJ, Oldham JM, Gramegna A, Stainer A, Mantero M, Sverzellati N, Lacedonia D, Richeldi L, Blasi F, Aliberti S. Walking the path of treatable traits in interstitial lung diseases. Respir Res 2023; 24:251. [PMID: 37872563 PMCID: PMC10594881 DOI: 10.1186/s12931-023-02554-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/05/2023] [Indexed: 10/25/2023] Open
Abstract
Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, and disease phenotypes. The identification of specific "treatable traits" in ILDs, which are clinically relevant and modifiable disease characteristics, may improve patient's outcomes. Treatable traits in ILDs may be classified into four different domains (pulmonary, aetiological, comorbidities, and lifestyle), which will facilitate identification of related assessment tools, treatment options, and expected benefits. A multidisciplinary care team model is a potential way to implement a "treatable traits" strategy into clinical practice with the aim of improving patients' outcomes. Multidisciplinary models of care, international registries, and the use of artificial intelligence may facilitate the implementation of the "treatable traits" approach into clinical practice. Prospective studies are needed to test potential therapies for a variety of treatable traits to further advance care of patients with ILD.
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Affiliation(s)
- Francesco Amati
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- IRCCS Humanitas Research Hospital, Respiratory Unit, Via Manzoni 56, 20089, Rozzano, Milan, Italy
| | - Paolo Spagnolo
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy
| | - Christopher J Ryerson
- Department of Medicine, University of British Columbia and Centre for Heart Lung Innovation, St. Paul's Hospital, Vancouver, Canada
| | - Justin M Oldham
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Andrea Gramegna
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Respiratory Unit and Cystic Fibrosis Adult Center, Milan, Italy
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Anna Stainer
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- IRCCS Humanitas Research Hospital, Respiratory Unit, Via Manzoni 56, 20089, Rozzano, Milan, Italy
| | - Marco Mantero
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Respiratory Unit and Cystic Fibrosis Adult Center, Milan, Italy
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Nicola Sverzellati
- Unit of Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Donato Lacedonia
- Department of Medical and Occupational Sciences, Institute of Respiratory Disease, Università degli Studi di Foggia, Foggia, Italy
| | - Luca Richeldi
- Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Blasi
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Respiratory Unit and Cystic Fibrosis Adult Center, Milan, Italy
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Stefano Aliberti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy.
- IRCCS Humanitas Research Hospital, Respiratory Unit, Via Manzoni 56, 20089, Rozzano, Milan, Italy.
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Guthrie J, Ko¨stel Bal S, Lombardo SD, Mu¨ller F, Sin C, Hu¨tter CV, Menche J, Boztug K. AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation. SCIENCE ADVANCES 2023; 9:eadg6375. [PMID: 37656781 PMCID: PMC10848965 DOI: 10.1126/sciadv.adg6375] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/01/2023] [Indexed: 09/03/2023]
Abstract
Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.
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Affiliation(s)
- Julia Guthrie
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Sevgi Ko¨stel Bal
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
| | - Salvo Danilo Lombardo
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Felix Mu¨ller
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Celine Sin
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Christiane V. R. Hu¨tter
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna BioCenter, A-1030 Vienna, Austria
| | - Jo¨rg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria
| | - Kaan Boztug
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
- St. Anna Children’s Hospital, Kinderspitalgasse 6, A-1090, Vienna, Austria
- Medical University of Vienna, Department of Pediatrics and Adolescent Medicine, Währinger Gürtel 18-20, A-1090 Vienna, Austria
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Sharma N, Kumar P, Shukla KS, Maheshwari S. AGE RAGE Pathways: Cardiovascular Disease and Oxidative Stress. Drug Res (Stuttg) 2023; 73:408-411. [PMID: 37308093 DOI: 10.1055/a-2047-3896] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
It is well established that Advanced Glycation End Products (AGEs) and their receptor (RAGE) are primarily responsible for the development of cardiovascular disease. As a result, diabetic therapy is very interested in therapeutic strategies that can target the AGE-RAGE axis. The majority of the AGE-RAGE inhibitors showed encouraging outcomes in animal experiments, but more information is needed to completely understand their clinical effects. The main mechanism implicated in the aetiology of cardiovascular disease in people with diabetes is oxidative stress and inflammation mediated by AGE-RAGE interaction. Numerous PPAR-agonists have demonstrated favourable outcomes in the treatment of cardio-metabolic illness situations by inhibiting the AGE-RAGE axis. The body's ubiquitous phenomena of inflammation occur in reaction to environmental stressors such tissue damage, infection by pathogens, or exposure to toxic substances. Rubor (redness), calor (heat), tumour (swelling), colour (pain), and in severe cases, loss of function, are its cardinal symptoms. When exposed, the lungs develop silicotic granulomas with the synthesis of collagen and reticulin fibres. A natural flavonoid called chyrsin has been found to have PPAR-agonist activity as well as antioxidant and anti-inflammatory properties. The RPE insod2+/animals underwent mononuclear phagocyte-induced apoptosis, which was accompanied with decreased superoxide dismutase 2 (SOD2) and increased superoxide generation. Injections of the serine proteinase inhibitor SERPINA3K decreased proinflammatory factor expression in mice with oxygen-induced retinopathy, decreased ROS production, and increased levels of SOD and GSH.
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Affiliation(s)
- Neeraj Sharma
- Department of Pharmacy, Bhagwant University, Ajmer, India
| | - Pavan Kumar
- Ph.D scholar of Department of Pharmacy, Bhagwant University, Ajmer, India
| | | | - Shubhrat Maheshwari
- Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, Rama University, Kanpur, Uttar Pradesh, India
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Rusciano D, Bagnoli P. Pharmacotherapy and Nutritional Supplements for Neovascular Eye Diseases. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1334. [PMID: 37512145 PMCID: PMC10383223 DOI: 10.3390/medicina59071334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/27/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
In this review, we aim to provide an overview of the recent findings about the treatment of neovascular retinal diseases. The use of conventional drugs and nutraceuticals endowed with antioxidant and anti-inflammatory properties that may support conventional therapies will be considered, with the final aim of achieving risk reduction (prevention) and outcome improvement (cooperation between treatments) of such sight-threatening proliferative retinopathies. For this purpose, we consider a medicinal product one that contains well-defined compound(s) with proven pharmacological and therapeutic effects, usually given for the treatment of full-blown diseases. Rarely are prescription drugs given for preventive purposes. A dietary supplement refers to a compound (often an extract or a mixture) used in the prevention or co-adjuvant treatment of a given pathology. However, it must be kept in mind that drug-supplement interactions may exist and might affect the efficacy of certain drug treatments. Moreover, the distinction between medicinal products and dietary supplements is not always straightforward. For instance, melatonin is formulated as a medicinal product for the treatment of sleep and behavioral problems; at low doses (usually below 1 mg), it is considered a nutraceutical, while at higher doses, it is sold as a psychotropic drug. Despite their lower status with respect to drugs, increasing evidence supports the notion of the beneficial effects of dietary supplements on proliferative retinopathies, a major cause of vision loss in the elderly. Therefore, we believe that, on a patient-by-patient basis, the administration of nutraceuticals, either alone or in association, could benefit many patients, delaying the progression of their disease and likely improving the efficacy of pharmaceutical drugs.
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Affiliation(s)
| | - Paola Bagnoli
- Department of Biology, University of Pisa, 56123 Pisa, Italy
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16
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Nardi M, Arceo MD, Ladenheim RI. [Analysis of the Argentine residency system from the paradigm of Complexity Sciences]. REVISTA DE LA FACULTAD DE CIENCIAS MÉDICAS 2023; 80:163-167. [PMID: 37402293 PMCID: PMC10443409 DOI: 10.31053/1853.0605.v80.n2.39843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/07/2023] [Indexed: 07/06/2023] Open
Abstract
Aim The objective is to carry out an analysis of the system of residences in Health in Argentina from the Theory of Complexity, in order to improve the understanding of the reality of this system, allowing an analysis to be carried out from another perspective different from the traditional. Source and synthesis of data In this review, the properties and characteristics of the residence system are analyzed according to the new paradigm of the Science of Complexity. For those who make decisions about the residential training of young professionals, full knowledge of the system in a holistic way and reflection on the characteristics of an open system applied to the residential system is of great importance and, it gives (or can give off) great benefits and possible improvements for those who live the day to day of these training programs. Conclusions It is important to mention the ultimate benefit that knowledge of the study system analyzed here has or can have: the possibility of multidisciplinarity, as one more step in the evolution of this type of system.
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17
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Mukerji M. Ayurgenomics-based frameworks in precision and integrative medicine: Translational opportunities. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e29. [PMID: 38550940 PMCID: PMC10953754 DOI: 10.1017/pcm.2023.15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 05/22/2023] [Accepted: 06/11/2023] [Indexed: 11/06/2024]
Abstract
In today's globalized and flat world, a patient can access and seek multiple health and disease management options. A digitally enabled participatory framework that allows an evidence-based informed choice is likely to assume an immense importance in the future. In India, traditional knowledge systems, like Ayurveda, coexist with modern medicine. However, due to limited crosstalk between the clinicians of both disciplines, a patient attempts integrative medicine by seeking both options independently with limited understanding and evidence. There is a need for an integrative medicine platform with a formalized approach, which allows practitioners from the two diverse systems to crosstalk, coexist, and coevolve for an informed cross-referral that benefits the patients. To be successful, this needs frameworks that enable the bridging of disciplines through a common interface with shared ontologies. Ayurgenomics is an emerging discipline that explores the principles and practices of Ayurveda combined with genomics approaches for mainstream integration. The present review highlights how in conjunction with different disciplines and technologies this has provided frameworks for (1) the discovery of molecular correlates to build ontological links between the two systems, (2) the discovery of biomarkers and targets for early actionable interventions, (3) understanding molecular mechanisms of drug action from its usage perspective in Ayurveda with applications in repurposing, (4) understanding the network and P4 medicine perspective of Ayurveda through a common organizing principle, (5) non-invasive stratification of healthy and diseased individuals using a compendium of system-level phenotypes, and (6) developing evidence-based solutions for practice in integrative medicine settings. The concordance between the two contrasting streams has been built through extensive explorations and iterations of the concepts of Ayurveda and genomic observations using state-of-the-art technologies, computational approaches, and model system studies. These highlight the enormous potential of a trans-disciplinary approach in evolving solutions for personalized interventions in integrative medicine settings.
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Affiliation(s)
- Mitali Mukerji
- Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur, Karwar, India
- School of Artificial Intelligence and Data Science (AIDE), Indian Institute of Technology Jodhpur, Karwar, India
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18
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Singh A, Ansari VA, Ansari TM, Hasan SM, Ahsan F, Singh K, Wasim R, Maheshwari S, Ahmad A. Consequence of Dementia and Cognitive Impairment by Primary Nucleation Pathway. Horm Metab Res 2023; 55:304-314. [PMID: 37130536 DOI: 10.1055/a-2052-8462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
An acquired loss of cognition in several cognitive domains that is severe enough to interfere with social or professional functioning is called dementia. As well as a moderately in-depth mental status examination by a clinician to identify impairments in memory, language, attention, visuospatial cognition, such as spatial orientation, executive function, and mood, the diagnosis of dementia requires a history evaluating for cognitive decline and impairment in daily activities, with confirmation from a close friend or family member. The start and organization of the cognitive assessment can be helped by short screening tests for cognitive impairment. Clinical presentations show that neurodegenerative diseases are often incurable because patients permanently lose some types of neurons. It has been determined through an assessment that, at best, our understanding of the underlying processes is still rudimentary, which presents exciting new targets for further study as well as the development of diagnostics and drugs. A growing body of research suggests that they also advance our knowledge of the processes that are probably crucial for maintaining the health and functionality of the brain. We concentrate on a number of the animal models of memory problems that have been mentioned in this review article because dementia has numerous etiologies. Serious neurological impairment and neuronal death are the main features of neurodegenerative illnesses, which are also extremely crippling ailments. The most prevalent neurodegenerative disorders are followed by those primary nucleation pathways responsible for cognitive impairment and dementia.
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Affiliation(s)
- Aditya Singh
- Faculty of Pharmacy, Integral University, Lucknow, India
| | | | | | | | - Farogh Ahsan
- Faculty of Pharmacy, Integral University, Lucknow, India
| | - Kuldeep Singh
- Faculty of Pharmacy, Integral University, Lucknow, India
| | - Rufaida Wasim
- Faculty of Pharmacy, Integral University, Lucknow, India
| | | | - Asad Ahmad
- Faculty of Pharmacy, Integral University, Lucknow, India
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19
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Pensotti A, Bertolaso M, Bizzarri M. Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool. Biomolecules 2023; 13:733. [PMID: 37238604 PMCID: PMC10216038 DOI: 10.3390/biom13050733] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 05/28/2023] Open
Abstract
A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as "tumor reversion". However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.
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Affiliation(s)
- Andrea Pensotti
- Research Unit of Philosophy of Science and Human Development, University Campus Bio-Medico of Rome, 00128 Rome, Italy
- Systems Biology Group Lab, Department of Experimental Medicine, Sapienza University, 00185 Rome, Italy
| | - Marta Bertolaso
- Research Unit of Philosophy of Science and Human Development, University Campus Bio-Medico of Rome, 00128 Rome, Italy
| | - Mariano Bizzarri
- Systems Biology Group Lab, Department of Experimental Medicine, Sapienza University, 00185 Rome, Italy
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20
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Sadegh S, Skelton J, Anastasi E, Maier A, Adamowicz K, Möller A, Kriege NM, Kronberg J, Haller T, Kacprowski T, Wipat A, Baumbach J, Blumenthal DB. Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond. Nat Commun 2023; 14:1662. [PMID: 36966134 PMCID: PMC10039912 DOI: 10.1038/s41467-023-37349-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/13/2023] [Indexed: 03/27/2023] Open
Abstract
A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.
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Affiliation(s)
- Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - James Skelton
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Elisa Anastasi
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Anna Möller
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nils M Kriege
- Faculty of Computer Science, University of Vienna, Vienna, Austria
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - David B Blumenthal
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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21
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Recanatini M, Menestrina L. Network modeling helps to tackle the complexity of drug-disease systems. WIREs Mech Dis 2023:e1607. [PMID: 36958762 DOI: 10.1002/wsbm.1607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/03/2023] [Accepted: 03/03/2023] [Indexed: 03/25/2023]
Abstract
From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
| | - Luca Menestrina
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
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22
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Abstract
Neurofibrillary tangles and plaques containing tau serve as the biological markers for Alzheimer disease (AD) and pathogenesis is widely believed to be driven by the production and deposition of the β-amyloid peptide (Aβ). The β-amyloid peptide (Aβ) that results from the modification of the amyloid precursor protein (APP) by builds up as amyloid deposits in neuronal cells. Thus, a protein misfolding process is involved in the production of amyloid. In a native, aqueous buffer, amyloid fibrils are usually exceedingly stable and nearly insoluble. Although amyloid is essentially a foreign substance made of self-proteins, the immune system has difficulty identifying and eliminating it as such for unknown reasons. While the amyloidal deposit may have a direct role in the disease mechanism in some disease states involving amyloidal deposition, this is not always the case. Current research has shown that PS1 (presenilin 1) and BACE (beta-site APP-cleaving enzyme) have - and -secretase activity that increases β-amyloid peptide (Aβ). Wealth of data has shown that oxidative stress and AD are closely connected that causes the death of neuronal cells by producing reactive oxygen species (ROS). Additionally, it has been demonstrated that advanced glycation end products (AGEs) and β-amyloidal peptide (Aβ) together increase neurotoxicity. The objective of this review is to compile the most recent and intriguing data of AGEs and receptor for advanced glycation end products (RAGE) pathways which are responsible for AD.
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Affiliation(s)
- Shubhrat Maheshwari
- Faculty of Pharmaceutical Sciences, Rama University, Kanpur, Uttar Pradesh, India
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23
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Singh A, Ansari VA, Mahmood T, Ahsan F, Wasim R, Shariq M, Parveen S, Maheshwari S. Receptor for Advanced Glycation End Products: Dementia and Cognitive Impairment. Drug Res (Stuttg) 2023. [PMID: 36889338 DOI: 10.1055/a-2015-8041] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
The pathophysiological processes of dementia and cognitive impairment are linked to advanced glycation end products (AGEs) and their receptor (RAGE).The neurofibrillary tangles (NFTs) of abnormally hyperphosphorylated tau protein and senile plaques (SPs), which are brought on by amyloid beta (Aβ) deposition, are the hallmarks of Alzheimer's disease (AD), a progressive neurodegenerative condition. Advanced glycation end products that are produced as a result of vascular dysfunction are bound by the receptor for advanced glycation end products (RAGE). Dementia and cognitive impairment could develop when RAGE binds to Aβ and produces reactive oxygen species, aggravating Aβ buildup and ultimately resulting in SPs and NFTs. RAGE could be a more powerful biomarker than Aβ because it is implicated in early AD. The resident immune cells in the brain known as microglia are essential for healthy brain function. Microglia is prominent in the amyloid plaques' outside border as well as their central region in Alzheimer's disease. Microglial cells, in the opinion of some authors, actively contribute to the formation of amyloid plaques. In this review, we first discuss the early diagnosis of dementia and cognitive impairment, and then detail the interaction between RAGE and Aβ and Tau that is necessary to cause dementia and cognitive impairment pathology, and it is anticipated that the creation of RAGE probes will help in the diagnosis and treatment of dementia and cognitive impairment.
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Affiliation(s)
- Aditya Singh
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, India
| | - Vaseem Ahamad Ansari
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, India
| | - Tarique Mahmood
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, India
| | - Farogh Ahsan
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, India
| | - Rufaida Wasim
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, India
| | - Mohammad Shariq
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, India
| | - Saba Parveen
- Department of Pharmacy, Faculty of Pharmacy, Integral University, Lucknow, India
| | - Shubhrat Maheshwari
- Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, Rama University, Mandhana, Bithoor Road, Kanpur, Uttar Pradesh, India
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Loscalzo J. Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning. FASEB J 2023; 37:e22660. [PMID: 36468661 PMCID: PMC10107166 DOI: 10.1096/fj.202201683r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 10/27/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.
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Affiliation(s)
- Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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25
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Saxe GN, Bickman L, Ma S, Aliferis C. Mental health progress requires causal diagnostic nosology and scalable causal discovery. Front Psychiatry 2022; 13:898789. [PMID: 36458123 PMCID: PMC9705733 DOI: 10.3389/fpsyt.2022.898789] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.
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Affiliation(s)
- Glenn N. Saxe
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
| | - Leonard Bickman
- Ontrak Health, Inc., Henderson, NV, United States
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Sisi Ma
- Program in Data Science, Department of Medicine, Clinical and Translational Science Institute, Institute for Health Informatics, School of Medicine, University of Minnesota, Minneapolis, MN, United States
| | - Constantin Aliferis
- Program in Data Science, Department of Medicine, Clinical and Translational Science Institute, Institute for Health Informatics, School of Medicine, University of Minnesota, Minneapolis, MN, United States
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Abstract
The current epistemology of autism as a phenotype derives from the consistency of historical accounts and decades of work within the tradition of descriptive epidemiology, culminating in current categorical descriptions within DSM and ICD nosologies and the concept of "prototypical autism." The demonstrated high heritability of this phenotype has led to an essentialist theory of autism as a biological entity and the concerted search within the developmental brain and genetic science for discrete biological markers. This search has not revealed simple markers explaining autistic outcomes and has led to moves towards a more dimensional account. This article proposes an alternative transactional approach. It proposes to understand autistic states as an emergent property within a complex developmental system; as the neurodivergent brain, and mind and body, encounter their social and physical environment within early development. Key evidence in support of this approach comes from random allocation intervention trials based on such transactional development theory, both in the infancy pre-diagnostic prodrome and the early post-diagnostic period. In replicated evidence, these intervention trials show that a targeted alteration in the quality of social transactional environment available for the child leads to significant, predictable, and sustained alterations in the outcome dimensional autistic phenotype over time; and further, in one prodromal trial, to a significant reduction in later categorical classification status. The inference from this evidence is that the prototypical autistic phenotype is to a degree malleable with a changed experienced social environment and that it is emergent from its constituent traits. Such a transactional approach enlarges our notion of the phenotype and brings the study of autism within mainstream individual difference developmental science. It challenges essentialist views, for instance as to intrinsic autistic "social avoidance" or theory of mind empathy deficits, integrates dimensional and categorical perspectives, and is consistent with the lived experience of autistic people and their advocacy for improved understanding within a social model.
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Affiliation(s)
- Jonathan Green
- Division of Psychology and Mental Health, Faculty of Biology Medicine and Health, Manchester Academic Health Sciences Centre, Royal Manchester Children’s Hospital, University of Manchester, Manchester, United Kingdom
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27
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Schuster J, Tollefson GA, Zarate V, Agudelo A, Stabila J, Ragavendran A, Padbury J, Uzun A. Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia. Front Genet 2022; 12:765985. [PMID: 35719905 PMCID: PMC9201216 DOI: 10.3389/fgene.2021.765985] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.
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Affiliation(s)
- Jessica Schuster
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
| | | | - Valeria Zarate
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Anthony Agudelo
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Joan Stabila
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Ashok Ragavendran
- Center for Computation and Visualization, Brown University, Providence, RI, United States
- Computational Biology of Human Disease, Brown University, Providence, RI, United States
| | - James Padbury
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
| | - Alper Uzun
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
- Computational Biology of Human Disease, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
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28
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Zhao H, Han B, Li X, Sun C, Zhai Y, Li M, Jiang M, Zhang W, Liang Y, Kai G. Salvia miltiorrhiza in Breast Cancer Treatment: A Review of Its Phytochemistry, Derivatives, Nanoparticles, and Potential Mechanisms. Front Pharmacol 2022; 13:872085. [PMID: 35600860 PMCID: PMC9117704 DOI: 10.3389/fphar.2022.872085] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/08/2022] [Indexed: 12/15/2022] Open
Abstract
Breast cancer is one of the most deadly malignancies in women worldwide. Salvia miltiorrhiza, a perennial plant that belongs to the genus Salvia, has long been used in the management of cardiovascular and cerebrovascular diseases. The main anti-breast cancer constituents in S. miltiorrhiza are liposoluble tanshinones including dihydrotanshinone I, tanshinone I, tanshinone IIA, and cryptotanshinone, and water-soluble phenolic acids represented by salvianolic acid A, salvianolic acid B, salvianolic acid C, and rosmarinic acid. These active components have potent efficacy on breast cancer in vitro and in vivo. The mechanisms mainly include induction of apoptosis, autophagy and cell cycle arrest, anti-metastasis, formation of cancer stem cells, and potentiation of antitumor immunity. This review summarized the main bioactive constituents of S. miltiorrhiza and their derivatives or nanoparticles that possess anti-breast cancer activity. Besides, the synergistic combination with other drugs and the underlying molecular mechanisms were also summarized to provide a reference for future research on S. miltiorrhiza for breast cancer treatment.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Yi Liang
- *Correspondence: Yi Liang, ; Guoyin Kai,
| | - Guoyin Kai
- *Correspondence: Yi Liang, ; Guoyin Kai,
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29
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Wang Y, Juan L, Peng J, Wang T, Zang T, Wang Y. Explore potential disease related metabolites based on latent factor model. BMC Genomics 2022; 23:269. [PMID: 35387615 PMCID: PMC8985251 DOI: 10.1186/s12864-022-08504-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 11/17/2022] Open
Abstract
Background In biological systems, metabolomics can not only contribute to the discovery of metabolic signatures for disease diagnosis, but is very helpful to illustrate the underlying molecular disease-causing mechanism. Therefore, identification of disease-related metabolites is of great significance for comprehensively understanding the pathogenesis of diseases and improving clinical medicine. Results In the paper, we propose a disease and literature driven metabolism prediction model (DLMPM) to identify the potential associations between metabolites and diseases based on latent factor model. We build the disease glossary with disease terms from different databases and an association matrix based on the mapping between diseases and metabolites. The similarity of diseases and metabolites is used to complete the association matrix. Finally, we predict potential associations between metabolites and diseases based on the matrix decomposition method. In total, 1,406 direct associations between diseases and metabolites are found. There are 119,206 unknown associations between diseases and metabolites predicted with a coverage rate of 80.88%. Subsequently, we extract training sets and testing sets based on data increment from the database of disease-related metabolites and assess the performance of DLMPM on 19 diseases. As a result, DLMPM is proven to be successful in predicting potential metabolic signatures for human diseases with an average AUC value of 82.33%. Conclusion In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. The results show that DLMPM has a better performance in prioritizing candidate diseases-related metabolites compared with the previous methods and would be helpful for researchers to reveal more information about human diseases.
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Affiliation(s)
- Yongtian Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China. .,Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an, China.
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.,Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an, China
| | - Tao Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.,Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an, China
| | - Tianyi Zang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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30
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Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
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31
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Leyton-Carcaman B, Abanto M. Beyond to the Stable: Role of the Insertion Sequences as Epidemiological Descriptors in Corynebacterium striatum. Front Microbiol 2022; 13:806576. [PMID: 35126341 PMCID: PMC8811144 DOI: 10.3389/fmicb.2022.806576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/04/2022] [Indexed: 11/20/2022] Open
Abstract
In recent years, epidemiological studies of infectious agents have focused mainly on the pathogen and stable components of its genome. The use of these stable components makes it possible to know the evolutionary or epidemiological relationships of the isolates of a particular pathogen. Under this approach, focused on the pathogen, the identification of resistance genes is a complementary stage of a bacterial characterization process or an appendix of its epidemiological characterization, neglecting its genetic components’ acquisition or dispersal mechanisms. Today we know that a large part of antibiotic resistance is associated with mobile elements. Corynebacterium striatum, a bacterium from the normal skin microbiota, is also an opportunistic pathogen. In recent years, reports of infections and nosocomial outbreaks caused by antimicrobial multidrug-resistant C. striatum strains have been increasing worldwide. Despite the different existing mobile genomic elements, there is evidence that acquired resistance genes are coupled to insertion sequences in C. striatum. This perspective article reviews the insertion sequences linked to resistance genes, their relationship to evolutionary lineages, epidemiological characteristics, and the niches the strains inhabit. Finally, we evaluate the potential of the insertion sequences for their application as a descriptor of epidemiological scenarios, allowing us to anticipate the emergence of multidrug-resistant lineages.
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32
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Chen R, Zheng J, Li L, Li C, Chao K, Zeng Z, Chen M, Zhang S. Metabolomics facilitate the personalized management in inflammatory bowel disease. Therap Adv Gastroenterol 2021; 14:17562848211064489. [PMID: 34987610 PMCID: PMC8721420 DOI: 10.1177/17562848211064489] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/15/2021] [Indexed: 02/04/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.
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Affiliation(s)
- Rirong Chen
- Division of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Jieqi Zheng
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, P.R. China
| | - Li Li
- Division of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou 510080, P.R. China
| | - Chao Li
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, P.R. China
| | - Kang Chao
- Division of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Zhirong Zeng
- Division of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China
| | - Minhu Chen
- Division of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou 510080, P.R. China
| | - Shenghong Zhang
- Division of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou 510080, P.R. China
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Verplaetse TL, Peltier MR, Roberts W, Burke C, Moore KE, Pittman B, McKee SA. Sex and alcohol use disorder predict the presence of cancer, respiratory, and other medical conditions: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions-III. Addict Behav 2021; 123:107055. [PMID: 34311184 PMCID: PMC8419091 DOI: 10.1016/j.addbeh.2021.107055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/23/2021] [Accepted: 07/16/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Women experience greater health consequences of alcohol compared to their male counterparts. In recent years, rates of drinking and heavy alcohol use have increased in women while remaining relatively steady in men. Thus, our aim was to newly examine associations between sex, AUD, and the presence of medical conditions in a large nationally representative, cross-sectional dataset. METHODS Using data from the U.S. National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III; n = 36,309), we evaluated relationships among sex and DSM-5 AUD, and their association with past year clinician-confirmed medical conditions. RESULTS Women were 1.5 to 2 times more likely to be diagnosed with a past year cancer, pain, respiratory, or other significant medical condition compared to men (odds ratio [OR] = 1.331-2.027). Individuals with an ongoing DSM-5 AUD were nearly 1.5 to 2 times more likely to report a confirmed past year liver, cardiovascular, cancer, or other significant medical condition compared to those without an AUD (OR = 1.437-2.073). Interactive effects demonstrated that women with an ongoing AUD were 2 to 3 times more likely to report a past year doctor- or health professional-confirmed medical condition compared to men; specifically, respiratory conditions and cancers (OR = 1.767-2.713). CONCLUSIONS Results identify that AUD is a critical factor associated with disease that spans organ systems. Associations between AUD and respiratory conditions or cancers are particularly robust in women. Effective interventions for a broad spectrum of medical conditions should consider the role of problematic alcohol use, especially given that rates of drinking in women are increasing.
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Affiliation(s)
- Terril L Verplaetse
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.
| | - MacKenzie R Peltier
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States; Psychology Service, Veterans Affairs Connecticut Healthcare System, West Haven, CT, United States.
| | - Walter Roberts
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.
| | - Catherine Burke
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.
| | - Kelly E Moore
- Department of Psychology, East Tennessee State University, Johnson City, TN, United States.
| | - Brian Pittman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.
| | - Sherry A McKee
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.
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Madeddu L, Grani G, Velardi P. Integrating categorical and structural proximity in Disease Ontologies. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2011-2014. [PMID: 34891682 DOI: 10.1109/embc46164.2021.9630114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related disease module (DM) in the interactome. We propose a methodology (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy.Clinical relevance- Disease ontologies are extensively used for diagnostic evaluation and clinical decision support but still reflect the clinical reductionist perspective. We demonstrate that the proposed network-based methodology allows us to analyze commonalities and differences among structural and categorical similarity of human diseases, help refine human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.
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35
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Prieto Santamaría L, García Del Valle EP, Zanin M, Hernández Chan GS, Pérez Gallardo Y, Rodríguez-González A. Classifying diseases by using biological features to identify potential nosological models. Sci Rep 2021; 11:21096. [PMID: 34702888 PMCID: PMC8548311 DOI: 10.1038/s41598-021-00554-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 10/14/2021] [Indexed: 11/25/2022] Open
Abstract
Established nosological models have provided physicians an adequate enough classification of diseases so far. Such systems are important to correctly identify diseases and treat them successfully. However, these taxonomies tend to be based on phenotypical observations, lacking a molecular or biological foundation. Therefore, there is an urgent need to modernize them in order to include the heterogeneous information that is produced in the present, as could be genomic, proteomic, transcriptomic and metabolic data, leading this way to more comprehensive and robust structures. For that purpose, we have developed an extensive methodology to analyse the possibilities when it comes to generate new nosological models from biological features. Different datasets of diseases have been considered, and distinct features related to diseases, namely genes, proteins, metabolic pathways and genetical variants, have been represented as binary and numerical vectors. From those vectors, diseases distances have been computed on the basis of several metrics. Clustering algorithms have been implemented to group diseases, generating different models, each of them corresponding to the distinct combinations of the previous parameters. They have been evaluated by means of intrinsic metrics, proving that some of them are highly suitable to cover new nosologies. One of the clustering configurations has been deeply analysed, demonstrating its quality and validity in the research context, and further biological interpretations have been made. Such model was particularly generated by OPTICS clustering algorithm, by studying the distance between diseases based on gene sharedness and following cosine index metric. 729 clusters were formed in this model, which obtained a Silhouette coefficient of 0.43.
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Affiliation(s)
- Lucía Prieto Santamaría
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain. .,Ezeris Networks Global Services S.L., 28028, Madrid, Spain.
| | | | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos, CSIC-UIB, 07122, Palma de Mallorca, Spain
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Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes. EPMA J 2021; 12:545-558. [PMID: 34642594 PMCID: PMC8495186 DOI: 10.1007/s13167-021-00254-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/01/2021] [Indexed: 12/12/2022]
Abstract
The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental "causal" role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a "historical" process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process - and not a static-stable condition - treatments should be tailored according to the "timing-frame" of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes - from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.
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Grani G, Madeddu L, Velardi P. A network-based analysis of disease modules from a taxonomic perspective. IEEE J Biomed Health Inform 2021; 26:1773-1781. [PMID: 34428165 DOI: 10.1109/jbhi.2021.3106787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The aim of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity in human-curated disease ontologies and proximity of disease modules in the human interactome network. We believe that the biomedical understanding of diseases is on the edge of a radical change. The disease module hypothesis (DMH), with its relevant applications to disease-gene discovery and drug repurposing, is leading the revolution of bio-medical research of the future. Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. However, the recent results of DMH have so far only marginally influenced the disease categorization principles. For these reasons, we deem it fundamental to systematically analyze the degree of correspondence between the anatomical and histological principles at the basis of current disease ontologies and the pathobiological similarity relations discovered in recent network-based studies. Towards this objective, we define a methodology and related algorithms to automatically induce a hierarchical structure of disease modules from proximity relations in the interactome network, and to align, label and systematically compare this structure with a manually defined disease ontology. We demonstrate that our study has some relevant clinical implications: To identify promising regions of the human interactome where new disease-gene relationships could be discovered, either exploiting data-driven methods or clinical experiments; To identify unexplored molecular relationships among diseases; To extend, correct and refine human-curated taxonomies. To the best of our knowledge, this is the first work that presents a methodology to systematically integrate taxonomic and network-based disease classification principles.
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Adeyeye SAO, Ashaolu TJ, Bolaji OT, Abegunde TA, Omoyajowo AO. Africa and the Nexus of poverty, malnutrition and diseases. Crit Rev Food Sci Nutr 2021; 63:641-656. [PMID: 34259104 DOI: 10.1080/10408398.2021.1952160] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
This review examines the nexus of poverty, malnutrition and diseases in Africa, the challenges, implications and their mitigation. The paper takes a critical look at available literatures on the primary causes, modes, implications and solutions to the problems of poverty, malnutrition and diseases in Africa continent. Poverty and malnutrition are outcomes of uncontrolled rapid population growth, inefficient agricultural and industrial practices, high debt profile of many African countries due to poor governance and corruption, diseases such as AIDS epidemic, malaria, Ebola virus and COVID-19 pandemic, poor and inadequate health infrastructure and armed conflicts. African poverty scenario entails non-availability of basic human needs which makes many Africans to be very poor. Despite abundance of natural resources, the gross domestic product per capita of many African countries is among the lowest of list of nations of the world. According United Nation in 2009, 22 of 24 nations among the "Low Human Development" nations of the world on the UN's Human Development Index were found in sub-Saharan Africa. Out of the 50 countries on the United Nation list of least developed countries, 34 of them were in Africa. According to FAO data over 200 million people in sub-Saharan Africa were undernourished in 2014-2016. The prevalence of undernourishment in sub-Saharan Africa rose from 181 million in 2010 to 222 million in 2016. In 2016, Africa had the highest prevalence of undernourishment in the world and estimated to be 20% of the population. While this was alarming in Eastern Africa where one-third of the population is suspected to be undernourished. In a similar data, World Bank also found that sub-Saharan Africa Poverty and Equity Data was 47% with over 500 million people in abject poverty in 2012. Poverty is the major cause of hunger and malnutrition in Africa while hunger and malnutrition escalated the problem of diseases in African continent. Poverty has continued to torment Africa as a result of poor and harmful economic policies, conflict and war, environmental factors like drought and climate change and population growth, poor leadership and greed. With the advent of COVID-19, the problem of poverty, malnutrition and diseases has been escalated and in many African countries people find it difficult to make ends meet.
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Affiliation(s)
- Samuel Ayofemi O Adeyeye
- Department of Food Technology, Hindustan Institute of Technology & Science, Hindustan University, Chennai, Tamil Nadu, India
| | - Tolulope J Ashaolu
- Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam.,Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang, Viet Nam
| | - Olusola T Bolaji
- Department of Food Technology, Lagos State Polytechnic, Ikorodu, Nigeria
| | | | - Adetola O Omoyajowo
- Department of Food Science & Technology, Federal University of Agriculture, Abeokuta, Nigeria
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Koyama E, Mundy C, Saunders C, Chung J, Catheline SE, Rux D, Iwamoto M, Pacifici M. Premature Growth Plate Closure Caused by a Hedgehog Cancer Drug Is Preventable by Co-Administration of a Retinoid Antagonist in Mice. J Bone Miner Res 2021; 36:1387-1402. [PMID: 33724538 PMCID: PMC9661967 DOI: 10.1002/jbmr.4291] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/25/2021] [Accepted: 03/12/2021] [Indexed: 12/15/2022]
Abstract
The growth plates are key engines of skeletal development and growth and contain a top reserve zone followed by maturation zones of proliferating, prehypertrophic, and hypertrophic/mineralizing chondrocytes. Trauma or drug treatment of certain disorders can derange the growth plates and cause accelerated maturation and premature closure, one example being anti-hedgehog drugs such as LDE225 (Sonidegib) used against pediatric brain malignancies. Here we tested whether such acceleration and closure in LDE225-treated mice could be prevented by co-administration of a selective retinoid antagonist, based on previous studies showing that retinoid antagonists can slow down chondrocyte maturation rates. Treatment of juvenile mice with an experimental dose of LDE225 for 2 days (100 mg/kg by gavage) initially caused a significant shortening of long bone growth plates, with concomitant decreases in chondrocyte proliferation; expression of Indian hedgehog, Sox9, and other key genes; and surprisingly, the number of reserve progenitors. Growth plate involution followed with time, leading to impaired long bone lengthening. Mechanistically, LDE225 treatment markedly decreased the expression of retinoid catabolic enzyme Cyp26b1 within growth plate, whereas it increased and broadened the expression of retinoid synthesizing enzyme Raldh3, thus subverting normal homeostatic retinoid circuitries and in turn accelerating maturation and closure. All such severe skeletal and molecular changes were prevented when LDE-treated mice were co-administered the selective retinoid antagonist CD2665 (1.5 mg/kg/d), a drug targeting retinoid acid receptor γ, which is most abundantly expressed in growth plate. When given alone, CD2665 elicited the expected maturation delay and growth plate expansion. In vitro data showed that LDE225 acted directly to dampen chondrogenic phenotypic expression, a response fully reversed by CD2665 co-treatment. In sum, our proof-of-principle data indicate that drug-induced premature growth plate closures can be prevented or delayed by targeting a separate phenotypic regulatory mechanism in chondrocytes. The translation applicability of the findings remains to be studied. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Eiki Koyama
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic Surgery, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104
| | - Christina Mundy
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic Surgery, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104
| | - Cheri Saunders
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic Surgery, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104
| | - Juliet Chung
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic Surgery, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104
| | - Sarah E. Catheline
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic Surgery, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104
| | - Danielle Rux
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic Surgery, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104
| | - Masahiro Iwamoto
- Department of Orthopaedic Surgery, School of Medicine, University of Maryland, Baltimore, Maryland 21201
| | - Maurizio Pacifici
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic Surgery, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104
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Giannoula A, Centeno E, Mayer MA, Sanz F, Furlong LI. A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities. Bioinformatics 2021; 37:1435-1443. [PMID: 33185649 DOI: 10.1093/bioinformatics/btaa964] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 09/16/2020] [Accepted: 11/03/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology. RESULTS In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces. AVAILABILITY AND IMPLEMENTATION https://gitlab.com/agiannoula/diseasetrajectories. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexia Giannoula
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Emilio Centeno
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Miguel-Angel Mayer
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, 08003, Barcelona, Spain
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A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02533-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Rai S, Bhatia V, Bhatnagar S. Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach. Comput Biol Chem 2021; 92:107505. [PMID: 34030115 DOI: 10.1016/j.compbiolchem.2021.107505] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/21/2021] [Accepted: 05/05/2021] [Indexed: 12/31/2022]
Abstract
Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associated diseases are required for treatment. 34 KEGG pathways targeted by lipid lowering drugs were used to construct a directed protein-protein interaction network and driver nodes were determined using CytoCtrlAnalyser plugin of Cytoscape 3.6. The involvement of driver nodes of HL in other diseases was verified using GWAS. The central nodes of the network and 34 overrepresented pathways had a critical role in Hyperlipidemia. The PI3K-AKT signalling pathway, non-essentiality, non-centrality and approved drug target status were the predominant features of the driver nodes. Next, a Random Forest classifier was trained on 1445 molecular descriptors calculated using PaDEL for 50 approved lipid lowering and 84 lipid raising drugs as the positive and negative training set respectively. The classifier showed average accuracy of 76.8 % during 5-fold cross validation with AUC of 0.79 ± 0.06 for the ROC curve. The classifier was applied to select molecules with favourable properties for lipid lowering from the 130 approved drugs interacting with the identified driver nodes. We have integrated diverse network data and machine learning to predict repurposing of nine drugs for treatment of HL associated diseases.
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Affiliation(s)
- Sneha Rai
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India; Department of Biotechnology, Noida Institute of Engineering and Technology, Greater Noida, India
| | - Venugopal Bhatia
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India; Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology Dwarka, New Delhi 110078, India.
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Constraints in Clinical Cardiology and Personalized Medicine: Interrelated Concepts in Clinical Cardiology. CARDIOGENETICS 2021. [DOI: 10.3390/cardiogenetics11020007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Systems biology is established as an integrative computational analysis methodology with practical and theoretical applications in clinical cardiology. The integration of genetic and molecular components of a disease produces interacting networks, modules and phenotypes with clinical applications in complex cardiovascular entities. With the holistic principle of systems biology, some of the features of complexity and natural progression of cardiac diseases are approached and explained. Two important interrelated holistic concepts of systems biology are described; the emerging field of personalized medicine and the constraint-based thinking with downward causation. Constraints in cardiovascular diseases embrace three scientific fields related to clinical cardiology: biological and medical constraints; constraints due to limitations of current technology; and constraints of general resources for better medical coverage. Systems healthcare and personalized medicine are connected to the related scientific fields of: ethics and legal status; data integration; taxonomic revisions; policy decisions; and organization of human genomic data.
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Diller GP, Arvanitaki A, Opotowsky AR, Jenkins K, Moons P, Kempny A, Tandon A, Redington A, Khairy P, Mital S, Gatzoulis MΑ, Li Y, Marelli A. Lifespan Perspective on Congenital Heart Disease Research: JACC State-of-the-Art Review. J Am Coll Cardiol 2021; 77:2219-2235. [PMID: 33926659 DOI: 10.1016/j.jacc.2021.03.012] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 12/19/2022]
Abstract
More than 90% of patients with congenital heart disease (CHD) are nowadays surviving to adulthood and adults account for over two-thirds of the contemporary CHD population in Western countries. Although outcomes are improved, surgery does not cure CHD. Decades of longitudinal observational data are currently motivating a paradigm shift toward a lifespan perspective and proactive approach to CHD care. The aim of this review is to operationalize these emerging concepts by presenting new constructs in CHD research. These concepts include long-term trajectories and a life course epidemiology framework. Focusing on a precision health, we propose to integrate our current knowledge on the genome, phenome, and environome across the CHD lifespan. We also summarize the potential of technology, especially machine learning, to facilitate longitudinal research by embracing big data and multicenter lifelong data collection.
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Affiliation(s)
- Gerhard-Paul Diller
- Department of Cardiology III-Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Muenster, Germany; Adult Congenital Heart Centre and National Centre for Pulmonary Hypertension, Royal Brompton and Harefield National Health Service Foundation Trust, Imperial College London, London, UK; National Register for Congenital Heart Defects, Berlin, Germany.
| | - Alexandra Arvanitaki
- Department of Cardiology III-Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Muenster, Germany; Adult Congenital Heart Centre and National Centre for Pulmonary Hypertension, Royal Brompton and Harefield National Health Service Foundation Trust, Imperial College London, London, UK; First Department of Cardiology, American Hellenic Educational Progressive Association University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Alexander R Opotowsky
- The Cincinnati Adult Congenital Heart Disease Program, Cincinnati Children's Hospital, Cincinnati, Ohio, USA; Heart Institute, Cincinnati Children's Hospital and University of Cincinnati, Cincinnati, Ohio, USA
| | - Kathy Jenkins
- Department of Cardiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Philip Moons
- Department of Public Health and Primary Care, Katholieke Universiteit Leuven, Leuven, Belgium; Institute of Health and Care Sciences, University of Gothenburg, Gothenburg, Sweden; Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Alexander Kempny
- Adult Congenital Heart Centre and National Centre for Pulmonary Hypertension, Royal Brompton and Harefield National Health Service Foundation Trust, Imperial College London, London, UK
| | - Animesh Tandon
- Pediatric Cardiology, Department of Pediatrics, University of Texas Southwestern Medical Center and Children's Health, Dallas, Texas, USA; Department of Radiology, University of Texas Southwestern Children's Medical Center, Dallas, Texas, USA
| | - Andrew Redington
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA; Montreal Heart Institute, Université de Montréal, Montreal, Québec, Canada
| | - Paul Khairy
- Montreal Heart Institute, Université de Montréal, Montreal, Québec, Canada
| | - Seema Mital
- Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Michael Α Gatzoulis
- Adult Congenital Heart Centre and National Centre for Pulmonary Hypertension, Royal Brompton and Harefield National Health Service Foundation Trust, Imperial College London, London, UK
| | - Yue Li
- Department of Computer Science, McGill University, Montréal, Québec, Canada
| | - Ariane Marelli
- McGill Adult Unit for Congenital Heart Disease Excellence (MAUDE Unit), Department of Medicine, McGill University, Montréal, Québec, Canada.
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Zhu T, Gong X, Bei F, Ma L, Sun J, Wang J, Qiu G, Sun J, Sun Y, Zhang Y. Primary immunodeficiency-related genes in neonatal intensive care unit patients with various genetic immune abnormalities: a multicentre study in China. Clin Transl Immunology 2021; 10:e1266. [PMID: 33777394 PMCID: PMC7984964 DOI: 10.1002/cti2.1266] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/01/2021] [Accepted: 02/28/2021] [Indexed: 11/10/2022] Open
Abstract
Objectives The present phenotype-based disease classification causes ambiguity in diagnosing and determining timely, effective treatment options for primary immunodeficiency (PID). In this study, we aimed to examine the characteristics of early-onset PID and proposed a JAK-STATopathy subgroup based on their molecular defects. Methods We reviewed 72 patients (< 100 days) retrospectively. These patients exhibited various immune-related phenotypes and received a definitive molecular diagnosis by next-generation sequencing (NGS)-based tests. We evaluated the PID-causing genes and clinical parameters. We assessed the genes that shared the JAK-STAT signalling pathway. We also examined the potential high risks related to the 180-day death rate. Results We identified PID disorders in 25 patients (34.72%, 25/72). The 180-day mortality was 26.39% (19/72). Early onset of disease (cut-off value of 3.5 days of age) was associated with a high 180-day death rate (P = 0.009). Combined immunodeficiency with associated or syndromic features comprised the most common PID class (60.00%, 15/25). Patients who presented life-threatening infections were most likely to exhibit PID (odds ratio [OR] = 2.864; 95% confidence interval [CI]: 1.047-7.836). Twelve out of 72 patients shared JAK-STAT pathway defects. Seven JAK-STATopathy patients were categorised as PID. They were admitted to NICUs as immunological emergencies. Most of them experienced severe infections and thrombocytopenia, with 4 succumbing to an early death. Conclusions This study confirmed that NGS can be utilised as an aetiological diagnostic method of complex immune-related conditions in early life. Through the classification of PID as pathway-based subtypes, we see an opportunity to dissect the heterogeneity and to direct targeted therapies.
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Affiliation(s)
- Tianwen Zhu
- Department of Neonatology Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Xiaohui Gong
- Department of Neonatology Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Fei Bei
- Department of Neonatology Shanghai Children's Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Li Ma
- Department of Neonatology Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jingjing Sun
- Department of Neonatology Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jian Wang
- Department of Medical Genetics and Molecular Diagnostic Laboratory Shanghai Children's Medical Center Shanghai Jiaotong University School of Medicine Shanghai China
| | - Gang Qiu
- Department of Neonatology Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jianhua Sun
- Department of Neonatology Shanghai Children's Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Yu Sun
- Department of Pediatric Endocrinology/Genetics Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai Institute for Pediatric Research Shanghai China
| | - Yongjun Zhang
- Department of Neonatology Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
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Hossain ME, Khan A, Moni MA, Uddin S. Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:745-758. [PMID: 31478869 DOI: 10.1109/tcbb.2019.2937862] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Disease prediction has the potential to benefit stakeholders such as the government and health insurance companies. It can identify patients at risk of disease or health conditions. Clinicians can then take appropriate measures to avoid or minimize the risk and in turn, improve quality of care and avoid potential hospital admissions. Due to the recent advancement of tools and techniques for data analytics, disease risk prediction can leverage large amounts of semantic information, such as demographics, clinical diagnosis and measurements, health behaviours, laboratory results, prescriptions and care utilisation. In this regard, electronic health data can be a potential choice for developing disease prediction models. A significant number of such disease prediction models have been proposed in the literature over time utilizing large-scale electronic health databases, different methods, and healthcare variables. The goal of this comprehensive literature review was to discuss different risk prediction models that have been proposed based on electronic health data. Search terms were designed to find relevant research articles that utilized electronic health data to predict disease risks. Online scholarly databases were searched to retrieve results, which were then reviewed and compared in terms of the method used, disease type, and prediction accuracy. This paper provides a comprehensive review of the use of electronic health data for risk prediction models. A comparison of the results from different techniques for three frequently modelled diseases using electronic health data was also discussed in this study. In addition, the advantages and disadvantages of different risk prediction models, as well as their performance, were presented. Electronic health data have been widely used for disease prediction. A few modelling approaches show very high accuracy in predicting different diseases using such data. These modelling approaches have been used to inform the clinical decision process to achieve better outcomes.
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Bolton C, Chen Y, Hawthorne R, Schepel IRM, Harriss E, Hofmann SC, Ellis S, Clarke A, Wace H, Martin B, Smith J. Systematic Review: Monoclonal Antibody-Induced Subacute Cutaneous Lupus Erythematosus. Drugs R D 2021; 20:319-330. [PMID: 32960413 PMCID: PMC7691410 DOI: 10.1007/s40268-020-00320-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background Subacute cutaneous lupus erythematosus (SCLE) lacks consensus diagnostic criteria and the pathogenesis is poorly understood. There are increasing reports of SCLE induced by monoclonal antibodies (mAbs), but there are limited data on the aetiology, clinical characteristics and natural course of this disease. Methods We devised a set of diagnostic criteria for SCLE in collaboration with a multinational, multispecialty panel. This systematic review employed a two-layered search strategy of five databases for cases of mAb-induced SCLE (PROSPERO registered protocol CRD42019116521). To explore the relationship between relative mAb use and the number of SCLE cases reported, the estimated number of mAb users was modelled from 2013 to 2018 global commercial data and estimated annual therapy costs. Results From 40 papers, we identified 52 cases of mAb-induced SCLE, occurring in a cohort that was 73% female and with a median age of 61 years. Fifty percent of cases were induced by anti-tumour necrosis factor (TNF)-ɑ agents. A median of three drug doses preceded SCLE onset and the lesions lasted a median of 7 weeks after drug cessation. Oral and topical corticosteroids were most frequently used. Of the licensed mAbs, adalimumab, denosumab, rituximab, etanercept and infliximab were calculated to have the highest relative number of yearly users based on global sales data. Comparing the number of mAb-induced SCLE cases with estimated yearly users, the checkpoint inhibitors pembrolizumab and nivolumab showed strikingly high rates of SCLE relative to their global use, but ipilimumab did not. Conclusion We present the first systematic review characterising mAb-induced SCLE with respect to triggers, clinical signs, laboratory findings, prognosis and treatment approaches. We identify elevated rates associated with the use of checkpoint inhibitors and anti-TNFɑ agents. Electronic supplementary material The online version of this article (10.1007/s40268-020-00320-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chrissy Bolton
- University College London, University College London Hospitals NHS Foundation Trust, London, UK. .,Medical Sciences Division, University of Oxford, Oxford, UK. .,Translational Gastroenterology Unit, Experimental Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
| | - Yifan Chen
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - Rachel Hawthorne
- John Radcliffe Hospital, Oxford University Hospitals NHS Trust, Oxford, UK
| | | | - Elinor Harriss
- Bodleian Health Care Libraries, The Knowledge Centre, Oxford University Old Road Campus Research Building, Oxford, UK
| | - Silke C Hofmann
- Department of Dermatology, Allergology and Dermatosurgery, HELIOS University Hospital Wuppertal, University of Witten/Herdecke, Wuppertal, Germany
| | - Spencer Ellis
- Lister Hospital, East and North Herts NHS Trust, Stevenage, UK
| | - Alexander Clarke
- The Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Helena Wace
- Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Blanca Martin
- Department of Dermatopathology, St John's Institute of Dermatology, St Thomas' Hospital, London, UK
| | - Joel Smith
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Digital Health for Enhanced Understanding and Management of Chronic Conditions: COPD as a Use Case. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11690-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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Delli Pizzi S, Granzotto A, Bomba M, Frazzini V, Onofrj M, Sensi SL. Acting Before; A Combined Strategy to Counteract the Onset and Progression of Dementia. Curr Alzheimer Res 2020; 17:790-804. [PMID: 33272186 DOI: 10.2174/1567205017666201203085524] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 09/10/2020] [Accepted: 10/16/2020] [Indexed: 11/22/2022]
Abstract
Brain aging and aging-related neurodegenerative disorders are posing a significant challenge for health systems worldwide. To date, most of the therapeutic efforts aimed at counteracting dementiarelated behavioral and cognitive impairment have been focused on addressing putative determinants of the disease, such as β-amyloid or tau. In contrast, relatively little attention has been paid to pharmacological interventions aimed at restoring or promoting the synaptic plasticity of the aging brain. The review will explore and discuss the most recent molecular, structural/functional, and behavioral evidence that supports the use of non-pharmacological approaches as well as cognitive-enhancing drugs to counteract brain aging and early-stage dementia.
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Affiliation(s)
- Stefano Delli Pizzi
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Alberto Granzotto
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Manuela Bomba
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Valerio Frazzini
- AP-HP, Epilepsy Unit, Pitie-Salpetriere Hospital and Brain and Spine Institute (INSERM UMRS1127, CNRS UMR7225, Sorbonne Universite), Pitie-Salpetriere Hospital, Paris, France
| | - Marco Onofrj
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
| | - Stefano L Sensi
- Behavioral Neurology and Molecular Neurology Units, Center for Advanced Studies and Technology, CAST, University G. d'Annunzio of Chieti-Pescara, Pescara, Italy
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Nobis A, Zalewski D, Waszkiewicz N. Peripheral Markers of Depression. J Clin Med 2020; 9:E3793. [PMID: 33255237 PMCID: PMC7760788 DOI: 10.3390/jcm9123793] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/09/2020] [Accepted: 11/19/2020] [Indexed: 12/22/2022] Open
Abstract
Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.
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Affiliation(s)
- Aleksander Nobis
- Department of Psychiatry, Medical University of Bialystok, pl. Brodowicza 1, 16-070 Choroszcz, Poland; (D.Z.); (N.W.)
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