Project Examples

Examples of potential CMBB Biotech Bridge Hackathon team projects:

1. Brain Health Challenge Examples

Create a Brain Longevity Risk Scoring Dashboard
Brain health factors are scattered across apps and hard for users to interpret.
Challenge:
Build an integrated dashboard that combines sleep, stress, movement, and cognitive performance data into a single “Brain Longevity Score.”
Stress Detection & Cortisol Spike Prediction
Chronic stress accelerates brain aging, and spikes in cortisol often occur before symptoms appear.
Challenge:
Use wearables, voice markers, or activity patterns to predict when a user’s stress is about to cross a threshold.
Create a Neuroinflammation Risk Scoring from Omics Data
Chronic neuroinflammation accelerates cognitive aging but is hard to measure directly.
Challenge:
Use public transcriptomics/proteomics datasets to build a risk score that approximates neuroinflammatory biological age.
Predict Brain Age from Multimodal Data
“Brain age” is a powerful marker of long-term cognitive resilience, yet models require expensive MRI.
Challenge:
Create a brain-age predictor using non-MRI sources: cognitive tests, sleep metrics, lifestyle data, or genetic risk scores.
Build a “Brain Aging Digital Twin”
Individuals lack tools to understand how their brain aging differs from the population.
Challenge:
Create a prototype of a digital twin that predicts future cognitive performance based on current lifestyle, sleep, and activity metrics. Allow users to simulate things such as “What if I sleep 1 hour more?” or “What if I exercise 3x/week?”
Determine Early Detection of Cognitive Decline from Everyday Digital Behavior
Mild cognitive impairment (MCI) often goes unnoticed until irreversible.
Challenge:
Build a model that detects early cognitive decline from passive signals — speech, typing cadence, reaction-time tests, or wearable data — using publicly available datasets or synthetic data.
Mitochondrial Dysfunction in Aging Neurons
Aged neurons show impaired ATP production, ROS imbalance, and reduced mitophagy.
Challenge:
Identify blood-based or digital biomarkers for neuronal mitochondrial dysfunction.
Model mitochondrial decline trajectories and intervention windows.
Blood–Brain Barrier (BBB) Integrity Decline
The BBB becomes leakier with age, allowing inflammatory cytokines and plasma proteins into the brain, accelerating neurodegeneration. Current methods for assessing BBB permeability are invasive or expensive.
Challenge
Develop non-invasive digital or molecular biomarkers or modeling tools to monitor BBB integrity over time.

2. Gut Health Challenge Examples

Personalize Nutrition for Health span Using the Microbiome
Most “personalized nutrition” tools ignore the microbiome and fail to improve long-term metabolic or inflammatory markers.
Challenge:
Create an algorithm or prototype platform that turns microbiome data into personalized recommendations that improve longevity-relevant outcomes.
Map and Modulate the Gut–Brain Axis for Cognitive Health
Microbial metabolites affect memory, mood, and neuroinflammation, but we lack practical tools to target this axis for cognitive longevity.
Challenge:
Identify gut-derived molecules or patterns linked to cognitive resilience and design a prototype intervention (e.g., scoring system, model, or therapeutic concept).
Predict Biological Age from the Gut Microbiome
Existing biological age clocks mostly rely on methylation or blood biomarkers; few use gut data despite its strong connection to aging.
Challenge:
Develop an explainable AI model that predicts biological age or health span metrics using microbiome composition and metabolite data.
Predict Microbiome–Drug Interactions for Polypharmacy
Older adults take multiple medications, many of which alter or are altered by the gut microbiome—causing side effects and reduced efficacy.
Challenge:
Build a tool that predicts high-risk microbiome–drug interactions using available datasets.
Strengthen the Gut Barrier (“Leaky Gut”) Before It Fails
Gut barrier breakdown drives systemic inflammation and accelerates aging, but we lack accessible biomarkers for early detection.
Challenge:
Create a minimally invasive biomarker, test, or model that assesses gut barrier integrity in real time.
Identify Microbial Signatures of Centenarians
Long-living populations have distinct microbiome traits, but these insights haven’t been turned into actionable tools.
Challenge:
Mine public datasets to extract microbial signatures of exceptional longevity and propose a translational use case.

3. Heart Health Challenge Examples

Early Detection of Cardiovascular Risk from Wearables
Most cardiovascular issues build silently over decades, but wearables can capture early signals.
Challenge:
Build a model that predicts elevated cardiovascular risk from passive data (HRV, resting HR, activity, sleep).
Heart Rate Variability (HRV) Biological Age Estimation
HRV correlates strongly with autonomic resilience and cardiovascular aging, but it isn't integrated into aging metrics.
Challenge:
Build an HRV-based biological age estimator using open datasets or sample data.
Biological Age of the Vasculature
There’s no easy proxy for arterial stiffness or endothelial function outside clinical settings.
Challenge:
Using activity, HR response, recovery time, and HRV, approximate a “vascular age” metric.
Create a Cardiovascular Health Score Dashboard
Heart health insights are scattered across apps, making it hard for users to see the big picture.
Challenge:
Integrate sleep, activity, HRV, stress, movement intensity, and recovery into a single “Cardiovascular Longevity Score” and personalized suggestions.
Detect Irregular Heart Patterns from Consumer Wearables
Consumer devices pick up anomalies, but false positives are frequent.
Challenge:
Build a noise-robust anomaly detector that flags meaningful irregularities (e.g., unusual tachycardia, HRV crashes) to improve signal quality without requiring medical-device precision.
Predict VO_₂max from Non-Exercise Data
VO_₂max is one of the strongest predictors of longevity, but hard to measure routinely.
Challenge:
Build a model to estimate VO₂max from passive data (HRV, step count, activity intensity, age, sleep).

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