Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

Abstract

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Keywords: artificial intelligence; blockchain; digital twin; machine learning; precision medicine; systems biology.

Figure 1

Number of multi-omics publications from 2002-2023 (PubMed database search: 31 December 2023). Data extracted from search terms [All Fields]: ‘multi-omics’; ‘genome’; ‘transcriptome’; ‘proteome’; ‘metabolome’; ‘microbiome’.

Figure 2

Overview of personalized medicine viewed through the lens of systems biology. Personalized health insights are derived from, and are interdependent on, layers including the genome, transcriptome, proteome, metabolome, and microbiome. Importantly, the responsiveness of each of these layers varies depending on several factors such as environmental exposures, social and behavioral activities, disease states, or health/medical interventions.

Figure 3

A conceptual workflow for the development of modular multi-omics health metrics. This framework outlines the transition from data acquisition and harmonization to the derivation of actionable health insights. The process is categorized into base, intermediate, and advanced knowledge tiers, emphasizing the systematic integration and interpretation of multi-omics data.

Figure 4

A representation of the Digital Twin framework in healthcare. On the left, an individual is mirrored by their digital counterpart, with ‘in silico’ modeling providing the computational foundation for this digital representation. On the right, the health trajectory of both the individual and their digital twin are charted over time, highlighting potential gaps in health metrics that can inform precision care and treatment strategies.

Figure 5

Flowchart of a private blockchain system in healthcare data management. Key components and processes are depicted as follows: The flowchart initiates with the input of patient data. Patient data undergo tokenization, converting sensitive information into secure data tokens. This process enhances privacy by ensuring that actual data elements are not directly exposed within the blockchain. External entities, such as researchers, healthcare professionals, and pharmaceutical companies, are shown requesting access to patient data. The mechanism for granting data access is depicted via a token exchange process. The use of smart contracts is highlighted, showing how they automate the decision-making process for data access, based on predefined criteria and permissions. In this process, patients actively manage their data-sharing preferences, granting or revoking permissions, which underscores patient autonomy in data management.