Multi-Omics Integration Analysis Service for Obesity Risk Prediction
InquiryScience Meets Personalization-Predict and Prevent Obesity with Multi-Omics
Protheragen offers a multi-omics integration analysis service for obesity risk prediction that leverages advanced omics technologies to analyze complex biological data. By utilizing multi-omics data, this approach helps to identify key biomarkers and molecular pathways involved in obesity, offering a personalized risk assessment and tailored intervention strategies. In the multi-omics integration analysis service for obesity risk prediction, various advanced technologies are utilized to integrate and analyze multiple layers of molecular data, including but not limited to:
Workflow of Multi-Omics Integration Analysis
Our analysis service provides a comprehensive approach to predicting obesity risk by combining advanced technologies from various omics fields. Through a step-by-step process, Protheragen delivers a risk prediction and intervention strategy, enabling more effective prevention and management of obesity.
Sample Collection
Biological samples such as blood, tissues, or body fluids (e.g., urine, saliva) are collected from individuals. These samples are used to generate multi-layered omics data, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics.
High-throughput Data Generation
Advanced technologies are used to generate various types of omics data. Genomics identifies genetic variations through next-generation sequencing (NGS), epigenomics analyzes DNA methylation markers, transcriptomics evaluates gene expression using RNA sequencing, proteomics detects protein levels through mass spectrometry, metabolomics identifies key metabolites, and microbiomics analyzes the composition of the gut microbiota.
Data Processing and Integration
Bioinformatics tools are used to process and integrate all omics data. Raw data is cleaned, mapped, and normalized to facilitate comparison and analysis across different omics types.
Advanced Bioinformatics and Machine Learning Analysis
Machine learning algorithms and statistical models are employed to analyze complex multi-omics data, identifying biomarkers, key molecular pathways, and their interactions related to obesity risk, revealing the underlying molecular mechanisms of individual obesity susceptibility.
Risk Prediction and Personalized Insights
Based on the integrated multi-omics data, predictive models are built to assess an individual's risk of developing obesity and related diseases (e.g., diabetes, cardiovascular diseases).
Applications
- Our service can provide a detailed assessment of an individual's genetic, epigenetic, metabolic, and microbiome factors to predict their risk of developing obesity and related metabolic conditions.
- Our service helps healthcare providers design individualized weight management plans based on the specific molecular profile of the individual, including diet, exercise, and lifestyle recommendations.
- Our service can identify early biomarkers for metabolic diseases such as diabetes and cardiovascular disorders, enabling timely interventions before clinical symptoms appear.
- Our service can support the development of personalized therapeutic strategies by identifying molecular targets that can be used for tailored pharmacological treatments, such as targeting specific genetic pathways.
Advantages
- Our service integrates data from genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, providing a holistic view of the biological mechanisms underlying obesity.
- By identifying molecular biomarkers of obesity at an early stage, our service enables preemptive interventions that reduce the likelihood of obesity-related diseases.
- Our service leverages cutting-edge bioinformatics and machine learning tools to process and analyze complex data, ensuring high accuracy and actionable insights.
- Our service provides personalized lifestyle, dietary, and therapeutic recommendations designed to address the specific metabolic and genetic factors contributing to an individual's obesity risk.
Multi-Omics Integration Analysis Service Used for Therapy Development
Multi-Omics Integration Analysis Service Used for Preclinical Studies
Pharmacodynamic and Pharmacokinetic Studies
Multi-omics supports biomarker discovery for pharmacodynamics (such as molecular biomarker analysis) and pharmacokinetics, improving evaluation metrics.
Publication Data
Technology: Genome-wide association studies (GWAS), RNA sequencing (RNA-seq), Mass spectrometry (MS), Nuclear magnetic resonance (NMR), 16S rRNA sequencing
Journal: Biomolecules
IF: 4.8
Published: 2021
Results: This study focuses on integrating multi-omics data (GWAS, EWAS, and biochemical data) with machine learning (ML) techniques to predict obesity-related insulin resistance (IR) in children. The results highlight that biochemical data provided the best predictive performance, followed by EWAS and GWAS datasets. XGBoost models achieved the most robust performance across all datasets, particularly when class imbalance was addressed through undersampling. Shapley additive explanations (SHAP) analysis was used to interpret the contribution of individual features, revealing that the leptin/adiponectin ratio, creatinine, and HDL levels were among the most important predictors for IR. The study emphasizes that combining biochemical and EWAS data may further improve predictive accuracy.
Fig.1 A Mendelian randomization study is analogous to a randomized trial. (Usova, et al., 2021)
Frequently Asked Questions
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Can the service help in managing other metabolic diseases?
Yes, the service also helps in identifying risks for other metabolic conditions like diabetes, cardiovascular diseases, and insulin resistance, allowing for targeted interventions beyond obesity.
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How accurate is the obesity risk prediction?
This service uses state-of-the-art machine learning models and large-scale data integration to provide highly accurate risk predictions based on the most current molecular insights.
Protheragen's multi-omics integration analysis service for obesity risk prediction offers a cutting-edge solution for those seeking personalized insights into their obesity risk. By integrating data from genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, this service provides a comprehensive analysis of the biological factors contributing to obesity. For more information, feel free to contact us and explore more information from us!
Reference
- Usova, E.; et al. Integrative analysis of multi-omics and genetic approaches—a new level in atherosclerotic cardiovascular risk prediction. Biomolecules. 2021, 11(11): 1597. (CC BY 4.0)
All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.