Precision-driven Obesity Prediction for Personalized Health Solutions

Protheragen utilizes several technologies, including next-generation sequencing (NGS), to simultaneously analyze multiple obesity-related genes, identifying pathogenic variants that influence obesity risk. By using single-nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS), Protheragen calculates a weighted genetic risk score (WGRS) to quantify genetic obesity risk, customizing the score based on different SNP sets (e.g., WGRS19 or WGRS32) to optimize prediction accuracy for specific populations. Additionally, ensemble-based machine learning algorithms, such as boosting, are used to develop predictive models that integrate genetic risk scores with clinical data, validated against independent datasets to ensure accuracy.

Workflow of Genetic Variant Panel-Assisted Obesity Prediction

The genetic variant panel-assisted obesity prediction model development service at Protheragen involves a comprehensive workflow from sample collection to the final development and validation of the prediction model. First, blood samples are collected and analyzed using NGS, with DNA extraction and the use of a custom gene panel to detect multiple obesity-related genes. Then, a WGRS is calculated based on the identified gene variants, and a machine-learning model is built by integrating other data. Finally, the model is validated with independent datasets, and Protheragen provides a detailed report to support personalized intervention strategies.

Sample Collection and Sequencing

DNA is extracted from blood samples and sequenced using NGS. Unique barcodes are assigned to samples to ensure data integrity during processing.

Gene Panel Analysis

Protheragen uses a custom-designed gene panel to analyze multiple obesity-related genes simultaneously. Data are compared against various databases to identify pathogenic or potentially pathogenic variants.

Risk Scoring and Model Development

Identified variants are used to calculate WGRS, and machine-learning models are built to integrate genetic and clinical data.

Validation and Reporting

The predictive model is validated using separate datasets to assess performance metrics like AUC. Protheragen provides a detailed report outlining the patient's genetic risk and recommendations for personalized intervention strategies.

Applications

• By understanding the genetic factors contributing to obesity, healthcare providers can tailor intervention strategies, such as specific dietary and exercise plans or pharmacological treatments, to each individual's needs.
• The prediction model can aid healthcare professionals in making more informed decisions regarding obesity management, especially in complex cases involving multiple genetic and environmental factors.
• The model also supports evaluating the risk of obesity-related comorbidities, such as type 2 diabetes and cardiovascular diseases, by providing a more comprehensive risk assessment based on genetic information.

Advantages

• Protheragen's panel includes a wide range of genes, covering both common and rare obesity-associated genetic variants.
• The use of advanced computational techniques ensures that the prediction model is robust, accurate, and capable of handling complex interactions between different risk factors.
• The results from the prediction model can help healthcare providers offer tailored prevention and intervention strategies, improving health outcomes for individuals at high risk of obesity.

Genetic Variant Panel Assisted Obesity Prediction Model for Therapy Development

Genetic variant panel-based prediction models provide insights into specific genetic factors influencing obesity, enabling more targeted and personalized therapy development.

Genetic Variant Panel Assisted Obesity Prediction Model for Preclinical Studies

In preclinical studies, genetic variant panel-based models enhance the relevance and accuracy of obesity models, pharmacological studies, and safety assessments.

Publication

Frequently Asked Questions

1. What is the purpose of the genetic variant panel-assisted obesity prediction model?

   The model is designed to predict an individual's risk of developing obesity based on their genetic information, using next-generation sequencing to analyze multiple genes linked to obesity. This helps in early identification and personalized treatment planning for those at high risk.

2. How does the prediction model work?

   The model works by calculating a WGRS from identified obesity-associated SNPs. The WGRS is then integrated with clinical data using machine learning algorithms to predict obesity risk with high accuracy.

3. How can the results be used in a clinical setting?

   The results can be used to develop personalized treatment plans for individuals at risk of obesity. Healthcare providers can use the insights to recommend specific lifestyle modifications, monitor at-risk patients more closely, and decide on the need for pharmacological interventions.

The genetic variant panel-assisted obesity prediction model development service at Protheragen analyzes multiple obesity-related genetic variants and integrates them with individual clinical data to predict obesity risk using machine learning models. For more information about our services and anti-obesity solutions, please feel free to contact us!

All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.

• Machine Learning-based Obesity Prediction Model Development Service