ArticleBriefings in bioinformatics2016
Systems medicine of inflammaging.
Article in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Inflammaging and Immunosenescence in the Post-COVID Era: Small Molecules, Big Challenges.ChemMedChem · 2025Review
- Article
- Telomere Length and Risk of Incident Fracture and Arthroplasty: Findings From UK Biobank.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2022Article
- Resilience integrates concepts in aging research.iScience · 2022Review
- Inflammaging and Brain: Curcumin and Its Beneficial Potential as Regulator of Microglia Activation.Molecules (Basel, Switzerland) · 2022Review
- Anti-aging Effects of Calorie Restriction (CR) and CR Mimetics based on the Senoinflammation Concept.Nutrients · 2020Review
- The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges.Frontiers in physiology · 2020Article
- Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine.Briefings in bioinformatics · 2019Review
- Multi-omic analysis of signalling factors in inflammatory comorbidities.BMC bioinformatics · 2018Article
- Network integration of multi-tumour omics data suggests novel targeting strategies.Nature communications · 2018Article
- Interdisciplinary approach towards a systems medicine toolbox using the example of inflammatory diseases.Briefings in bioinformatics · 2017Article
- Potential therapeutic effects of the MTOR inhibitors for preventing ageing and progeria-related disorders.British journal of clinical pharmacology · 2016Review
- Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules.Scientific reports · 2016Article
- Inflammation-a Critical Appreciation of the Role of Myeloid Cells.Microbiology spectrum · 2016Review
- Inflammation Thread Runs across Medical Laboratory Specialities.Mediators of inflammation · 2016Review
- Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes.Genes & nutrition · 2015Article
- The Interplay of Aging and the Immune System.Missouri medicineReview
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.