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2021 Catalyst Awards Results Announcement

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Results Announcement

2021 Catalyst Awards – Awarded Projects

Exploring the potential of metformin in population longevity
Shiu Lun Ryan AU YEUNG | School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong

Fingerprinting Exhaled Volatile Compounds in Aging-Related Preclinical Heart Failure Using Breath Biopsy
Erik FUNG | Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong

Development of a smartphone app to predict and visualize risk of developing chronic diseases: integration of genetic risk and wearable data
Youngwon KIM | School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong

Unraveling potential drug targets for longevity using sex-specific Mendelian randomization
Man Ki KWOK | School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong

A Directed Graph Neural Network-based Drug-Repurposing Approach to Identify a Lead Combination of Drugs for Alzheimer’s Disease
Victor O.K. LI | Department of Electrical and Electronic Engineering, The University of Hong Kong


 

 


Exploring the potential of metformin in population longevity

Principal Investigator:
Shiu Lun Ryan Au Yeung, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Co-Investigators:
Shan Luo1, Ian C.K. Wong2,3,4 , C Mary Schooling1,5

1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
2Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
3Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
4Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China
5School of Public Health and Health Policy, City University of New York, New York, USA

Abstract:
Metformin, a first line medication for type 2 diabetes in the general population, has substantial potential for promoting healthy aging since it activates AMP activated protein kinase (AMPK), a key player in the anti-aging signalling pathway. However, existing approaches to investigate this hypothesis, such as studies of the oldest old and pharmacoepidemiologic studies are subjected to various degree of biases. To circumvent these limitations in previous studies, we will use genomics relevant to metformin actions in UK Biobank to interrogate the potentials of metformin in promoting healthy aging, with telomere length as an objective proxy. If the potential effect of metformin in longevity is genetically validated, this can provide stronger grounds in assessing the role of metformin in longevity using randomized controlled trials. If metformin was found to promote healthy aging, this would revolutionize current clinical practice given metformin is a highly affordable, well established medication with a good safety profile, and hence can substantially facilitate the use of the medication to improve aging compared to other investigational drugs.

 


 


Fingerprinting Exhaled Volatile Compounds in Aging-Related Preclinical Heart Failure Using Breath Biopsy

Principal Investigator:
Erik Fung, Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

Co-Investigator(s):
Susanna S. S. Ng1; Gloria H. W. Lau1; Leong Ting Lui1

1Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong

Abstract:
Heart failure (HF) is predominantly an aging-related disorder that affects at least 1-2% of the general population. As a leading cause of hospital readmission, it will increase in numbers with global aging. First diagnosis of HF often occurs as life-threatening cardiorespiratory distress, although patients may have had weeks to years of functional decline and symptoms, including easy fatigability, shortness of breath, leg swelling and chest discomfort. In the natural biology of aging, cardiac muscles stiffen as ventricular relaxation and filling are impaired (i.e. diastolic dysfunction (DD)). Biochemical changes in the heart and vessels are detectable in the blood circulation and in exhaled breath that precede cardiac imaging findings. Past studies have demonstrated early metabolic shifts in the heart from utilization of fatty acids to ketones and pentanes. These and other volatile organic compounds (VOCs) may be detectable in preclinical HF but have not been explored. Early detection of HF remains difficult, as it requires detailed history-taking, physical examination and investigations. However, a biochemical window of opportunity to catch aging-related preclinical HF with primarily DD (i.e. HF with preserved ejection fraction or HFpEF) could have a profound impact on early detection. This proof-of-concept study draws on recallable study participants from two longitudinal cohorts including over 1,200 older adults with cardiac imaging, biomarker and other high-dimensional datasets, and will aid in the discovery of VOCs that could indicate preclinical HFpEF. Upon validation, these efforts could lead to the development of a cost-effective biomarker panel for screening at-risk older adults in the community.

 


 


Development of a smartphone app to predict and visualize risk of developing chronic diseases: integration of genetic risk and wearable data

Principal Investigator:
Youngwon Kim, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Co-Investigator(s):
Shiu Lun Ryan Au Yeung1, Brian Hon-Yin2, Michael Multhaup3

1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
2Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
3Guardant Health, Redwood City, California, United States

Abstract:
Preventing or delaying the onset of common chronic diseases through healthy lifestyles is a key priority. The role of personalized digital healthcare services is being increasingly recognized in clinical settings as well as consumer markets. Prominent wearable devices have now enabled numerous people to track their lifestyle including energy expenditure, steps, and dietary intake. At the same time, direct-to-consumer genetic testing services raised their popularity. Such a contemporary plethora of information on wearable-device lifestyle indicators and genetic risk provides an unprecedented opportunity not only to promote a healthy lifestyle, but also to predict and prevent development of chronic disease outcomes. We aim to develop an app that visualizes individuals’ unique risk of chronic diseases by integrating genetic and lifestyle information. Our mobile app, which is evidence-based but user-friendly, will allow users to constantly monitor how their own disease risk progresses according to their wearable-measured lifestyle indicators. We believe that this app will grow as a communication platform for sharing personal stories and achievements on lifestyle change and disease risk reduction. The disease prediction of our app will be based on a series of algorithms developed and validated by our research team. Importantly, our mobile app platform will enable our users to directly integrate their wearable data from the manufacturing companies via API, and upload genetic risk estimates from their medical practitioners and/or direct-to-consumer genetic testing companies.

 


 


Unraveling potential drug targets for longevity using sex-specific Mendelian randomization

Principal Investigator:
Man Ki KWOK, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Co-Investigator(s):
C Mary Schooling1

1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Abstract:
The gender gap in longevity highlights the potential of sex-specific etiological insights to identify more effective pharmaceutical interventions. Women generally have lived longer than men worldwide since the mid-18th century. Sex differences in chronic disease patterns, lifestyle and healthcare seeking behaviour may all contribute to lifespan discrepancy. From an evolutionary biology perspective, longevity trades off against reproduction such that maximising reproductive success may be at the expense of susceptibility to chronic diseases. Explicating underlying biological drivers of longevity separately in men and women would shed light on interventions for promoting healthy aging and longevity. This pioneering project aims to identify drivers, with a focus on potentially druggable protein targets, of longevity in men and women using sex-specific Mendelian randomization. Exploiting ‘big data’ of large extensively genotyped genetic associations using Mendelian randomization has opened up new opportunities for timely and cost-effective drug discovery and repurposing. By using genetic variants as instrumental variables to assess causal associations, Mendelian randomization provides less biased estimates to inform the causal roles of potentially druggable exposures. This work will firstly identify potentially sex-specific druggable proteins relevant to longevity in Western populations. If substantiated in Asian populations in the future, our systematic search of relevant protein drug targets for longevity would accelerate drug development tailored-made for men and women, thereby contributing to close the gender gap in longevity globally.

 


 


A Directed Graph Neural Network-based Drug-Repurposing Approach to Identify a Lead Combination of Drugs for Alzheimer’s Disease

Principal Investigator:
Victor O.K. Li, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China

Co-Investigator(s):
Jacqueline Lam1, Jocelyn Downey1, Illana Gozes2, Yang Han1, Tushar Kaistha1

1Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
2Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Israel

Abstract:
Worldwide, around 50 million people are suffering from Alzheimer’s Disease (AD) and related forms of dementia, resulting in 28.8 million disability-adjusted life-years (DALYs), posing a significant threat on human longevity and quality of life globally. To date, no effective disease-modifying treatment or preventative therapies have been found, while the search for effective drug candidates is lengthy and data-constrained. To address this challenge, we propose a novel, ground-breaking AI-driven Directed Graph Neural Network (GNN)-based Drug-Repurposing Approach, capitalizing on the association of somatic mutations in AD pathology and the identification of 272 very long gene targets. Our approach will embed the 272 protein pathway data, and make use of relevant available big genetic and drug datasets, to determine a lead combination of effective drug candidates that interacts with mutation phenotype either directly or through network-based actions. Our novelties include: (1) a directed GNN drug-repurposing approach to identify drug candidates; (2) domain-specific somatic mutations/genes incorporated into the biomedical graph to determine a lead combination of candidate drugs that interacts with somatic mutation phenotype either directly or through network-based actions; (3) domain-specific genetic directed pathways and long genes incorporated; (4) knowledge of co-morbidities of AD incorporated; (5) a lead combination of effective drugs, instead of single drugs investigated; (6) a causal model integrated to validate the lead combination of candidate drugs and confirm its impacts on genes, proteins, and behaviours, associated with AD. This longevity- and quality-of-life-driven AI drug-repurposing study will significantly accelerate the process and precision of AD drug identification.

Results Announcement