Multiomic approach boosts disease prediction accuracy beyond traditional methods

By Vijay Kumar MalesuMar 26 2024Reviewed by Susha Cheriyedath, M.Sc.

In a recent study published in the journal Nature Aging, researchers assessed the added predictive value of integrating polygenic risk scores (PRSs) and gut microbiome scores with conventional risk factors for common diseases in a long-term cohort study.

Analysis: Integration of polygenic and gut metagenomic risk prediction for common diseases. Image Credit: remotevfx.com / Shutterstock

Background 

Multiomic technologies are transforming disease prediction by integrating genomic and microbiomic data, offering new insights into age-related conditions like heart disease, diabetes, and cancer. Previously, risk assessments relied mainly on demographic, lifestyle, and clinical metrics. Now, the integration of PRSs and gut microbiome analysis into risk models promises to improve predictive accuracy beyond traditional factors. PRSs provide a cost-effective genetic predisposition metric, while the gut microbiome adds a novel dimension to understanding disease risk. This emerging approach necessitates further research to refine its accuracy and ensure its effectiveness across various populations and healthcare systems.

About the study 

The FINRISK 2002 cohort, part of a series of Finnish surveys aimed at exploring chronic disease risk factors since 1972, served as the foundation for this study, focusing on the interplay between gut microbiota and health outcomes. Spanning six Finnish regions, this cohort engaged 8,783 participants from a pool of 13,498 invitees, including a diverse demographic aged 25–74. Under stringent ethical guidelines, these participants underwent comprehensive health examinations and contributed biological samples, including blood and stool.

This research, grounded in detailed baseline data collection, aimed to explore the predictive power of genetic and microbiomic factors alongside traditional risk indicators for diseases like coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer's disease (AD), and prostate cancer. Through careful sample handling and state-of-the-art genomic and metagenomic analyses, the study capitalized on advanced multiomic technologies to build predictive models. These models were refined through rigorous statistical methods, evaluating their predictive performance against conventional risk assessment tools.

Study results 

In the FINRISK 2002 cohort, a longitudinal study spanning over 17.8 years and including electronic health records (EHRs), 579 of T2D, 333 cases of CAD, 273 of AD, and 141 of prostate cancer were identified among participants with both imputed genotypes and gut metagenomic sequencing. The baseline clinical risk factors exhibited significant differences between incident cases and non-cases for CAD, T2D, and AD, with certain factors like smoking for T2D and sex, diastolic blood pressure (DBP), and High-Density Lipoprotein (HDL) for AD not differing significantly. Prostate cancer cases differed significantly from non-cases in terms of baseline age and smoking habits.

PRSs and conventional risk factors were assessed for their predictive performance in incident diseases through Cox regression models. The analysis revealed that PRSs, when assessed individually or in combination with conventional risk factors, significantly correlated with incident diseases, enhancing the predictive performance beyond baseline clinical risk factors alone. Notably, for diseases like CAD, T2D, and prostate cancer, PRSs offered a distinct advantage over traditional family history indicators, emphasizing their potential to complement existing risk assessment models.

Subanalyses exploring additional risk factors, such as glucose levels determined through nuclear magnetic resonance (NMR) for T2D, consistently supported the PRSs' predictive value. The gut microbiome also emerged as a significant factor, with its composition at baseline correlating with incident diseases. The study delved into the gut microbiome's diversity and its association with disease incidence, finding specific patterns that could potentially enhance disease prediction models.

The research underscored the potential of integrating polygenic, metagenomic, and conventional factors into a cohesive model for predicting incident diseases. Such a model, which combines PRSs and gut microbiome scores with conventional risk factors, showed a marked improvement in predictive accuracy for CAD, T2D, AD, and prostate cancer. This integrative approach illustrates the promise of multiomic data in refining disease prediction and tailoring preventive measures more effectively.

Subgroup analyses reaffirmed the significant associations between PRSs, gut microbiome scores, and disease incidence, highlighting these factors' contributions across different conditions. 

Conclusions 

To summarize, this study contrasts the predictive power of well-established PRSs, baseline gut microbiome, and traditional risk factors across a median follow-up of 17.8 years. Findings reveal that while age stands as the most influential individual risk factor for CAD, AD, and prostate cancer, the inclusion of PRSs and gut microbiome scores notably enhances predictive accuracy. PRSs alone significantly correlate with higher disease incidence, underscoring their potential to augment conventional risk assessments. Furthermore, the study suggests that PRSs can refine predictions for CAD, T2D, and prostate cancer, even beyond family history's established risk implications. Although the gut microbiome's predictive contribution appears modest, it shows promise in enhancing disease forecasts when combined with conventional factors. The analysis points to a subtle role of the gut microbiome across different conditions, suggesting that its predictive value may vary due to the complex interplay between host aging and microbial changes.