Integrated Risk Assessment Coupled with Molecular Detection Platform Enhance Personalized Health Care in Metabolic Disorders

Authors

  • Sylvester Ndimele Department of Environmental Health Science, School of Health Sciences and Practice, New York Medical College, Valhalla, NY, 10595, USA.
  • Miranda L. Carpenter Department of Environmental Health Science, School of Health Sciences and Practice, New York Medical College, Valhalla, NY, 10595, USA.
  • Jae-Hyeon Cho Institute of Agriculture and Life Science, Collegeof Veterinary Medicine, Gyeongsang National University, Jinju, 660-701, Korea.
  • Diane E. Heck Department of Environmental Health Science, School of Health Sciences and Practice, New York Medical College, Valhalla, NY, 10595, USA.
  • Hong Duck Kim Department of Environmental Health Science, School of Health Sciences and Practice, New York Medical College, Valhalla, NY, 10595, USA.

DOI:

https://doi.org/10.9734/bpi/etdhr/v3/1913A

Keywords:

Metabolic syndrome, Type 2 diabetes, epigenomics, proteomics, metabolomics, single nucleotide polymorphism, beta cell sequencing

Abstract

Type 2 Diabetes mellitus (T2DM) is characterized by multifaceted metabolic dysfunction resulting from alteration of endothelial and beta cell dysfunction, and regulatory molecular dysfunction under insulin signaling network rooted stress, lifestyle, diet, and physical inactivity. It is caused by both environmental and genetic variations which could be engaged in the onset of secondary disease as a risk barrier associated with micro level vascular diseases such as coronary artery disease, cardiovascular disease hypertension, obesity, stroke, and dyslipidemia. Furthermore, it is important to note that T2DM is a risk factor for the development of mental illness with cognitive impairment such as Alzheimer's disease (AD). Emerging evidence indicates that interrelation between metabolic syndrome and mental disorder linked several molecular interfaces and connectivity using integrated multi-omics approaches. To understand better pathobiology in metabolic illness and improve prediction skill set of T2DM with complicated genetic background and heterogeneity traits, this review summarizes that these categories include core omics strategy, genomics, transcriptomic, and proteomics; advanced omics application such as nutrigenomics, and metabolomics. In the present, diagnostic markers such as glucose concentrations and hemoglobin A1c are used to predict the status of metabolic surveillance in the blood. Emerging post genomic era evolved with the human whole genome sequencing project. Diet and lifestyle has shown dual features to be used in care of personalized dietary medicine as therapeutic direction and precision medicine for health surveillance depend on different genetic variation by illustrated gene -diet interaction. Molecular based risk assessment utilizing Omics tools like nutrigenomics and metabolomics give new insights beneficial to prediction and prevention in health complications due to environmental risk and exposure to the population who adapted to the new post-industrialization niche.

Published

2022-02-14

How to Cite

Sylvester Ndimele, Miranda L. Carpenter, Jae-Hyeon Cho, Diane E. Heck, & Hong Duck Kim. (2022). Integrated Risk Assessment Coupled with Molecular Detection Platform Enhance Personalized Health Care in Metabolic Disorders. Emerging Trends in Disease and Health Research Vol. 3, 94–101. https://doi.org/10.9734/bpi/etdhr/v3/1913A