Advanced analytical methods needed for better insights into chronic diseases microbiologystudy

An international team of researchers has explored how multi-omics—the integration of molecular data across different biological layers—can enhance our understanding of the way genetic and environmental factors interact to influence chronic diseases.

Their review, recently published in Human Genomics, highlights how advancements in multi-omics technologies are helping to uncover the biological mechanisms driving non-communicable diseases (NCDs) which account for more than 74% of global deaths and can include cardiovascular diseases, cancers, diabetes, and chronic respiratory conditions.

The scoping review co-led by Dr. Robel Alemu, a Visiting Research Fellow at the University of Adelaide Medical School (postdoctoral researcher at UCLA) and NHMRC Emerging Leadership Fellow Associate Professor Azmeraw Amare examines extensive literature in the field to assess how multi-omics techniques are advancing research into these diseases, the challenges in integrating complex datasets, and the urgent need for greater diversity in genomic and biomedical research.

“Non-communicable diseases are driven by a combination of genetic predispositions and environmental exposures, such as diet, pollution, and physical activity; how these factors function together is referred to as gene-environment (GxE) interactions, which plays a significant role in determining disease risk and treatment responses,” said Dr. Alemu.

“In some cases, a person’s genetic makeup can alter how environmental exposures impact disease risk (like how certain genetic variations are associated with an increased risk of Parkinsons’ in people exposed to specific pesticides).

“In other cases, environmental exposures influence which genetic factors contribute to disease risk (the impact of the FTO gene on a body mass index will vary depending on lifestyle factors).”

By integrating multi-omics datasets—such as genomics (DNA), epigenomics (molecular markers that regulate gene activity), proteomics (proteins), metabolomics (biochemical processes)—scientists can develop a more complete picture of how biological systems interact to influence health and disease.

Multi-omics approaches are transforming precision medicine, helping researchers develop more targeted treatments and prevention strategies.

For example, recent studies have identified specific genes that protect brain cells from damage caused by oxidative stress, a key factor in various neurodegenerative diseases, while in pharmacogenomics, multi-omics research is enabling personalized medicine, such as using BRCA1/2 genetic testing to guide treatment decisions for breast cancer patients who may benefit from targeted therapies.

However, a major challenge is the lack of diversity in biomedical research, which limits the ability of findings to apply more generally.

“For instance, 85% of genome-wide association studies (GWAS) primarily involve individuals of European ancestry, leading to significant disparities in polygenic score (PGS) predictive accuracy for other genetic ancestries,” Dr. Alemu said.

“Expanding genomic diversity can not only address these inequities but also lead to the discovery of clinically important variants, as seen in research on African ancestry uncovering critical insights into kidney disease and cholesterol regulation.

“Other challenges in multi-omics research include high costs, computational complexity, and the need for advanced tools to integrate large, diverse datasets.”

The review calls for international collaborations and the development of equity-centered computational methods to enhance data integration and ensure that scientific advances benefit all populations.

“Recent advances in AI and machine learning have the potential to revolutionize multi-omics research by integrating large-scale, complex datasets and uncovering complex and novel biological insights,” said Associate Professor Amare.

“However, challenges such as data bias, lack of model transparency, and privacy concerns must be addressed to ensure responsible and effective use.

“By developing AI-driven approaches that are equitable and transparent, we can unlock new possibilities for personalized medicine and disease prevention.”

The authors advocate for expanding multi-omics research in underrepresented populations, strengthening local research infrastructure in low-and middle-income countries, and developing global standards for data sharing and integration to accelerate discoveries and improve health outcomes.