Metabolic Syndromes at IgMin Research | Medicine Group
Our vision is to promote the integration of scientific fields to hasten the discovery process.
About
Metabolic Syndromes encompass a cluster of conditions that significantly increase the risk of cardiovascular diseases, type 2 diabetes, and other chronic health problems. These conditions, which include abdominal obesity, elevated blood pressure, high blood sugar levels, and abnormal cholesterol levels, are linked to insulin resistance and systemic inflammation. Researchers in the field of Metabolic Syndromes focus on understanding the genetic, molecular, and lifestyle factors contributing to these disorders to develop targeted prevention and treatment strategies.
The study of Metabolic Syndromes integrates insights from endocrinology, cardiology, and nutrition science to address the multifactorial nature of these conditions. By exploring areas such as the impact of diet, physical activity, and pharmacological interventions, research in this field aims to reduce the prevalence and complications of metabolic disorders. Advancements in this area are crucial for improving public health outcomes, enhancing quality of life, and preventing the onset of related diseases.
Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
Which articles are now trending?
Research Articles
- Development of a Mechanical Seal Closed Design Model
- Ammonia: A Trend of Dry Deposition in Vietnam
- Cyber Threat Analysis (CTA) in Current Conflicts
- Preparing for SpaceX Mission to Mars
- Maternal Knowledge and Practices in Caring for Children under Five with Pneumonia: A Cross-Sectional Study in Vietnam
- Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information
