Gabriela Ortiz Cruz

Dr. Gabriela Ortiz Cruz

orcid

Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 September 2026 in Nutrients
Verified
Journal Article
Open access
PDF file
Rafael Gomes AraújoRafael Gomes Araújo
Rafael Gomes Araújo
GCGabriela Ortiz Cruz
Gabriela Ortiz Cruz
SNSunday Sedodo Nupo
Sunday Sedodo Nupo
YCYulianna Cordero
Yulianna Cordero
LCLauro Cortes-Hernandez
Lauro Cortes-Hernandez
STSandra Lucía Teniente
Sandra Lucía Teniente
Type 2 diabetes mellitus (T2D) represents over 90% of diabetes cases globally, posing a growing public health crisis with an estimated 783 million adults projected to be affected by 2045. This condition arises from a complex interplay of insulin resistance, progressive pancreatic β-cell dysfunction, and chronic low-grade inflammation, strongly influenced by genetic predisposition, obesity, sedentary lifestyle, and poor dietary habits. While genome-wide association studies (GWAS) have identified key susceptibility loci, including TCF7L2, PPARG, and KCNJ11, these varian0ts explain only ~10% of T2D heritability, underscoring the critical role of gene–environment interactions. This review synthesizes current evidence on the genetic, nutrigenomic, and epigenetic mechanisms underlying T2D pathogenesis and progression. We examine how dietary components, such as flavonoids, terpenoids, alkaloids, and bioactive compounds from natural products, modulate gene expression via transcription factors and influence insulin sensitivity, oxidative stress, and inflammation. Furthermore, we explore the emerging role of epigenetic modifications, including DNA methylation, histone acetylation, and chromatin remodeling, in mediating the lasting effects of hyperglycemia and environmental factors on T2D risk. The potential of epigenetic biomarkers for early diagnosis, patient stratification, and personalized therapy is discussed. Finally, we highlight how next-generation sequencing and artificial intelligence, particularly machine learning and deep learning models, are advancing multi-omics data integration, enabling improved T2D subtype classification, prognosis prediction, and the development of precision nutrition and pharmacoepigenetic strategies.