! The results of this competition will not be benchmarked automatically. Each submission will be reviewed manually by a jury of experts.
Human Lifespans
The upcoming QInnovision world challenge provided an inspiring context to explore the potential entanglement of factors influencing healthy ageing for the general population - and use novel paradigms to leverage quantum features to model the causal relations and evaluate potential life prolonging interventions and measures.
Background and Introduction
Merck Healthcare, a division of Merck KGaA, Darmstadt, Germany, is living the vision of improving and prolonging the lives of millions of patients around the world. In this spirit, we sponsor the QInnovision Olympian Lifespan Longevity Challenge.
For this hackathon we aim at identifying behavioral and interventional approaches for healthy aging of body, mind and spirit. Focus is therefore on exercise, epigenetics, and cognition [1], inspired by the National Academy of Medicine's (NAM) insights on global aging [2], research on the role of endocrine changes in aging [3], studies on the diet impact on aging [4], and the potential anti-aging effects of supplements [5, 8].
The NAM roadmap [6, 7] emphasizes the need for proper nutrition in older adults, highlighting the importance of adequate protein intake, essential vitamins, and minerals to support muscle and bone health. It also notes the role of micronutrients in protecting against cellular damage and supporting immune function, particularly for older athletes. Further studies evaluate the impact.
Challenge Overview:
Our goal is to bring together bright minds to tackle the most pressing issues related to aging and to foster collaborative innovation for a healthier, longer lifespan in our and future generations.
As Participant you will develop models and analytical tools to investigate the drivers for longevity and healthy aging and identify the most important healthy aging modifiers, including diet, exercise, supplements, and mindsets. This multidisciplinary challenge will require the team to harness data science, high-performance computing, and evaluate potential added value using quantum computing methodologies.
Potential Operations:
Data Collection and Cleaning: Gather and preprocess data on longevity, healthy lives, athletes, including performance metrics, health outcomes. Make use of scientific literature, preclinical, clinical, medical, molecular target and open data sets. If you want to do big data spidering / brute force on hpc, you do not need to restrict to athletes.
Statistical Analysis: Employ statistical methods to identify correlations / mechanisms for longevity, controlling for confounding factors such as diet, training regimens, and genetic predispositions.
Machine Learning Modeling: Develop predictive models to assess the potential impact of different lifestyles / interventions / treatments / mindsets on (athlete) longevity and health indicators. Evaluate the potential advantage of Quantum Machine Learning in that context, given likely low volume of data points. If the problem gets too complex, restrict on few key drivers per category.
Quantum Computing Simulations: Utilize quantum computing for complex simulations, potentially exploring molecular mechanisms by which different interventions could influence aging processes. Or use Quantum (enhanced) Generative Models (yes, we know the maturity and limits of the potential solutions).
Visualization and Interpretation: Create visualizations to present findings and interpret the implications of different approaches for athlete health, performance, and longevity.
Further Reading:
Outcome
If the team produces novel and significant modelling insights, Merck will consider a publication grant in a relevant journal.
References
- Rea 2017, Towards ageing well: Use it or lose it: Exercise, epigenetics and cognition, https://link.springer.com/article/10.1007/s10522-017-9719-3
- Fried 2022, A global roadmap to seize the opportunities of healthy longevity, https://www.nature.com/articles/s43587-022-00332-7
- Kalyani 2023, Endocrinology of the Aging Patient, https://doi.org/10.1016/j.ecl.2022.12.001
- Pathak 2023, Ketogenic Diets and Mitochondrial Function: Benefits for Aging But Not for Athletes, https://pubmed.ncbi.nlm.nih.gov/36123723/
- Roskoff 2023, Multivariate genome-wide analysis of aging related traits identifies novel loci and new drug targets for healthy aging, https://www.nature.com/articles/s43587-023-00455-5
- Leonhardt 2024, Dietary Intake of Athletes at the World Masters Athletics Championships as Assessed by Single 24 h Recall. Nutrients 2024, 16, 564. https://doi.org/10.3390/nu16040564
- Armand 2024, Optimizing longevity: Integrating Smart Nutrition and Digital Technologies for Personalized Anti-aging Healthcare, https://ieeexplore.ieee.org/document/10463262
- Espinoza 2023, Drugs Targeting Mechanisms of Aging to Delay Age-Related Disease and Promote Healthspan: Proceedings of a National Institute on Aging Workshop, https://doi.org/10.1093/gerona/glad034
Potential Data Sources to be used
- World Health Organization (WHO) Database: For general health and longevity data.
- International Olympic Committee (IOC) Athlete Database: Provides comprehensive data on Olympic athletes, including performance metrics and participation records.
- PubMed and Google Scholar: For scientific studies on medical aspects on longevity, and athletic performance.
- ClinicalTrials.gov: For ongoing or completed clinical trials involving drug, supplement and mechanistic interventions, which may provide relevant data on its effects. Howto: https://classic.clinicaltrials.gov/ct2/resources/download
- Global Sports Archive: Offers detailed statistics on sports performances worldwide, which could be useful for performance analysis.
- The Molecular Map of Exercise - Molecular Transducers of Physical Activity Consortium (MoTrPAC) is a national research consortium. Its goal is to study the molecular changes that occur in response to exercise, and ultimately to advance the understanding of how physical activity improves and preserves health. We aim to generate a molecular map of the effects of exercise. https://motrpac-data.org/
References / tools
Network learning
For potential quantum algorithms
Use whatever you find fits best your needs
Note:
As this challenge is explorative and quantum algorithms and hardware are still limited in their capacity, we are open to alternative solutions that might emerge during working on the challenge at the hackathon. If the team detects alternatives that could add value to the analytics of the data, understanding of correlations, and leveraging the capabilities of different methods, ranging from Bayesian inference, reservoir computing to graphical reasoning / QNLP… these can be explored, too, wherever they make sense.
While we are interested in the maturity and capability and potential of hybrid quantum hpc approaches, we are also keen to see if /how any non-quantum proposals would perform in the task in comparison (considering e.g. inference and real time data processing of e.g. wearable data, or required training effort of the multi modal models for upcoming data, but also the sparsity of data and trainability of models in dependency of their hyperparameters). We are keen to see hybrid solutions that combine best of all worlds and promise to make an impact on health.
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