Introduction:

Obesity and type 2 diabetes (T2D) are major public health concerns worldwide, associated with increased morbidity, mortality, and healthcare costs[1,2]. These metabolic disorders result from complex interactions between genetic predispositions, environmental exposures, and lifestyle factors, making their pathogenesis highly intricate. In this context, genomic and bioinformatic approaches have emerged as essential tools to unravel the molecular underpinnings of these conditions[3–5].

The advent of next-generation sequencing (NGS) technologies has revolutionized our ability to identify thousands of genetic variants linked to obesity and diabetes, including single nucleotide polymorphisms (SNPs) and structural variants[6]. These discoveries have provided valuable insights into metabolic pathways and revealed potential biomarkers for early diagnosis and risk stratification[7]. Longitudinal studies and large population cohorts have further contributed robust data for the identification of genetic signatures associated with complex metabolic phenotypes[8].

Simultaneously, the development of sophisticated bioinformatic pipelines capable of handling large-scale omics datasets has enabled integrative analyses that offer a more holistic understanding of these disorders[9]. This synergy between genomics and bioinformatics is driving a paradigm shift from traditional biomedical models toward precision medicine, where interventions are increasingly personalized and effective[10].

Building upon genomic discoveries, structural bioinformatics is essential for the functional characterization of disease-associated variants by mapping them onto three-dimensional protein structures [11]. This type of approach enables the identification of alterations in key structural domains, binding pockets, or allosteric sites that may affect protein stability, dynamics, or molecular interactions. Tools such as homology modeling, molecular docking, and molecular dynamics simulations allow for the in silico exploration of how specific polymorphisms or post-translational modifications influence protein behavior in the context of obesity and T2D [12,13]. This systems structural biology perspective reinforces the translational impact of bioinformatic approaches, helping to bridge potential gaps between genotype and phenotype [14]. Genomics Applied to Obesity and Diabetes
Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic basis of obesity and T2D, identifying multiple loci linked to increased disease risk[15,16]. Among the most notable are the FTO (fat mass and obesity-associated) gene, which influences energy balance, and TCF7L2 (transcription factor 7-like 2), a key regulator of insulin secretion and glucose homeostasis[16]. These findings have shed light on fundamental biological mechanisms, such as adipocyte differentiation and insulin sensitivity[17].

However, despite these advances, the identified genetic variants explain only a fraction of the heritability of these diseases, a phenomenon known as the "missing heritability"[18]. This gap suggests the involvement of rare variants with larger effects, as well as gene-gene and gene-environment interactions that remain largely uncharacterized[19]. Functional genomics, integrating data on gene expression, epigenetic regulation, and three-dimensional genome architecture, offers promising avenues to address these limitations.

Additionally, novel technologies such as whole-genome sequencing (WGS) and single-cell RNA sequencing (scRNA-seq) are enabling unprecedented resolution in mapping gene networks and metabolic pathways implicated in obesity and T2D[6,16]. These approaches facilitate the identification of specific cell populations affected by disease processes and reveal intra-tissue molecular heterogeneity, critical for developing more targeted and effective therapies[20] (figure 1).

Figure 1: Overview of genomic approaches in obesity and type 2 diabetes (T2D). GWAS have identified key risk loci, including FTO and TCF7L2, linked to adipocyte differentiation and insulin sensitivity. The concept of missing heritability highlights the role of rare variants and gene-environment interactions. Functional genomics, together with WGS and scRNA-seq, enables deeper insight into regulatory networks and supports the development of targeted therapies.

Bioinformatics and Integrated Omics
While genomic data alone provide valuable information, they are often insufficient to fully capture the complexity of metabolic disorders[21]. Thus, integrating multiple layers of omics data, including transcriptomics, epigenomics, proteomics, and metagenomics, has become essential in obesity and diabetes research[21,22]. This systems biology approach allows for the characterization of not only genetic predispositions but also dynamic changes induced by environmental and behavioral factors[23].

Numerous bioinformatics tools and pipelines have been developed to support the integration and analysis of these massive datasets[24]. These resources enable the construction of gene-protein interaction networks, identification of co-expression modules, and prediction of altered metabolic pathways[22]. For instance, combining DNA methylation and transcriptomic profiles has elucidated regulatory mechanisms involved in insulin resistance and adipose tissue accumulation[25,26].

The Table 1 below highlights some key bioinformatic tools used in multi-omics integration for obesity and diabetes research, describing their functions and applications[27–31]:

Table 1: Key Bioinformatic Tools for Multi-Omics Integration in Obesity and Diabetes Research.

Molecular Modeling and Protein Dynamics

Molecular modeling plays a pivotal role in elucidating the structural features of key proteins involved in the pathophysiology of obesity and T2D[15,16]. Techniques such as homology modeling, molecular docking, and molecular dynamics simulations enable the prediction of three-dimensional protein structures, even when crystallographic data are unavailable[32]. These analyses are instrumental for identifying potential therapeutic targets and for rational drug design[33]. In the context of T2D, structural modeling has been widely applied to investigate interactions between the insulin receptor (INSR) and its ligands, as well as to study regulatory proteins like the glucose transporter GLUT4[34,35]. Similarly, in obesity research, the structural characterization of nuclear receptors involved in adipogenesis, such as PPARγ, has provided critical insights into molecular mechanisms and pharmacological intervention opportunities[36,37].

Molecular dynamics simulations add a temporal dimension to structural studies, allowing the assessment of protein conformational stability and ligand-binding dynamics under physiological conditions[38,39]. These approaches can uncover alterations in structural flexibility due to genetic variants or post-translational modifications, directly impacting protein function[40]. Thus, molecular modeling and dynamics offer a robust framework for exploring new therapeutic avenues in obesity and diabetes[39].

Protein structures and small-molecule ligands can be retrieved from well-established databases such as the RCSB Protein Data Bank (https://www.rcsb.org/) and PubChem (https://pubchem.ncbi.nlm.nih.gov/), respectively. Once collected, these molecular entities can be explored through in silico approaches such as molecular docking and molecular dynamics simulations to investigate their interactions, binding affinities, and conformational behavior in biologically relevant environments (figure 2). These computational strategies are especially valuable for examining protein–ligand and protein–protein interactions, offering mechanistic insights that complement experimental data. In the context of metabolic disorders, such analyses contribute to a deeper understanding of the structural determinants governing molecular recognition and function, ultimately guiding the rational design of targeted therapeutics.

Figure 2:Simplified pipeline for molecular modeling applied to obesity and T2D research. Structures are retrieved from public databases, modeled if necessary, and used in docking and molecular dynamics simulations to explore biomolecular interactions and guide therapeutic development.

Future Perspectives
The integration of genomic, transcriptomic, epigenomic, and proteomic data with clinical information is paving the way for implementing precision medicine in the management of obesity and diabetes[22]. This approach aims to personalize therapeutic strategies based on each individual's molecular and genetic profile, promoting more effective interventions with fewer adverse effects[10]. The development of genetic risk panels and machine learning-based predictive algorithms exemplifies this promising trend[41].

However, translating these technologies into clinical practice still faces significant challenges, such as methodological standardization, functional interpretation of variants of uncertain significance (VUS), and ethical issues related to genetic data privacy[42]. Moreover, population representation in genomic studies remains uneven, with minority ethnic groups often underrepresented, potentially limiting the generalizability of findings and exacerbating health disparities.

Looking ahead, continuous advances in sequencing technologies, coupled with the development of more efficient bioinformatic tools, are expected to enable even more detailed characterization of the molecular mechanisms underlying metabolic diseases. Interdisciplinary collaboration among geneticists, bioinformaticians, clinicians, and public health professionals will be crucial to translate these insights into concrete benefits for the prevention and treatment of obesity and diabetes.
Conclusion
The application of genomic and bioinformatic approaches has significantly advanced our understanding of the molecular mechanisms underlying obesity and diabetes, uncovering new risk loci, altered metabolic pathways, and potential therapeutic targets. The integration of multi-omics data and the use of sophisticated computational tools have expanded the capacity to identify complex interactions between genetic, epigenetic, and environmental factors, contributing to the shift toward precision medicine.

Despite these achievements, several important challenges remain, including the need for greater population diversity in genomic studies, the functional interpretation of rare variants, and the effective clinical translation of bioinformatic discoveries. Strengthening interdisciplinary collaboration, alongside ongoing technological development, will be essential for transforming genomic knowledge into personalized interventions that enhance the prevention, diagnosis, and treatment of obesity and diabetes, with broad implications for global public health.

Author contributions MCSA: Conceptualization-methodology, performed the literature search, writing original draft. RCCS: Preparation of the figures e writing original draft. . All authors contributed to the review and editing of the manuscript.

Funding No funding received for this work.

Data availability Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Clinical trial number not applicable.

Declarations

Ethical approval No ethical approval was required for this study.

Conflict of interest The authors declare that they have no conflict of interest.

Abbreviations: T2D: Type 2 Diabetes; NGS: Next-Generation Sequencing; GWAS: Genome-Wide Association Studies; SNP: Single Nucleotide Polymorphism; FTO: Fat Mass and Obesity-Associated Gene; TCF7L2: Transcription Factor 7-Like 2; WGS: Whole-Genome Sequencing; scRNA-seq: Single-Cell RNA Sequencing; IPA: Ingenuity Pathway Analysis; MOFA: Multi-Omics Factor Analysis; INSR: Insulin Receptor; GLUT4: Glucose Transporter Type 4; PPARγ: Peroxisome Proliferator-Activated Receptor Gamma; VUS: Variant of Uncertain Significance.