Approved research

Computational analyses of genotypic and phenotypic data with treatment response from patients with multimorbidity and the role of inflammation as a driver of multimorbidity.

Lay summary

Hospitals in UK see 40-50% older patient, who are experiencing shorter life expectancy and increasing number of severities, which starts to affect their function and quality of healthy life years. Such patients suffer from multiple chronic conditions (multimorbidity). Current clinical care of multi-morbidities is based largely on the guidelines for treating the single diseases separately, and patients with multimorbidity are frequently excluded from clinical trials. We hypothesize that stratification can aid disease management and significantly improve the present poorly served clinical practices for multimorbid patients. Also, most of the current therapies don't address the nexus between inflammation and multiple chronic conditions in a patient. To tailor the best therapy, a trade-off between mortality risk and disability risk must be mutually agreed between patient and therapist. As a result, we would like to come up with risk score-based strategies to achieve the same. Machine Learning (ML) based algorithms have been widely used for prediction/classification problems in bioinformatics. With the continued deployment of advanced high-throughput omics technologies (specially NGS) in clinical practice, AI offers significant opportunities to assist with the analysis of the terabytes of clinical and omics data being generated from patients. The project will require 6 months of data pre-processing, followed by 2 years of rigorous application of AI/ML computational techniques and finally 6 months of collation of results.

Scope extension:

1. To identify potential biomarkers (genomic, molecular, imaging, etc.) that can sub-classify, ultimately better diagnose and better inform treatment regimens for patients with multimorbidity.
2. To find clusters of multimorbidity across the UK based on demography and discover any correlations of those clusters with factors such as socioeconomic factors, diet, lifestyle, etc.
3. GWAS analysis of multimorbid clusters from point 1 and 2 for identifying risk loci for multimorbid clusters.
4. Gene network and pathway analysis of multimorbid clusters from point 1 and 2 to identify novel gene networks and pathways associated with multimorbid clusters.
5. To unravel the effectiveness of anti-inflammatory drugs on the stratified classes (from point 1 and 2), which target inflammatory pathways, such as p38 MAPK, IL-6/JAK/STAT3, PI3K, and Hippo pathway.
6. Risk stratification for patients to the trade-off between mortality and disability risk due to inflammation in multimorbid patients.
7. To identify better treatment regimens using the biological assay analysis for patient with comorbidity and/or multimorbidity.

Scope extension: 

1. To identify potential biomarkers (genomic, molecular, imaging, etc.) that can sub-classify, ultimately better diagnose and better inform treatment regimens for patients with multimorbidity.
2. To find clusters of multimorbidity across the UK based on demography and discover any correlations of those clusters with factors such as socioeconomic factors, diet, lifestyle, etc.
3. GWAS analysis of multimorbid clusters from point 1 and 2 for identifying risk loci for multimorbid clusters.
4. Gene network and pathway analysis of multimorbid clusters from point 1 and 2 to identify novel gene networks and pathways associated with multimorbid clusters.
5. To unravel the effectiveness of anti-inflammatory drugs on the stratified classes (from point 1 and 2), which target inflammatory pathways, such as p38 MAPK, IL-6/JAK/STAT3, PI3K, and Hippo pathway.
6. Risk stratification for patients to the trade-off between mortality and disability risk due to inflammation in multimorbid patients.
7. To identify better treatment regimens using the biological assay analysis for patient with comorbidity and/or multimorbidity.
8. To investigate features/variables affecting the susceptibility to comorbidity and/or multimorbidity.
9. To investigate features/variables responsible for aging and health decline in multimorbid patients.