{"signature": "0xa1a4564c6c6b18e7657eeae472756074cb0c4eb86d4d1159ed9587168f7b5ec3192de59b263467afe57986631470d227ba173dd907dd88752f1f2ac569a399891b", "application": {"round": "0x0C0412DDB846096Ea1e37de717921EBF4fEF9A39", "answers": [{"type": "email", "answer": "liviazaharia2020@gmail.com", "hidden": true, "question": "Email Address", "questionId": 0}, {"type": "paragraph", "answer": "personal funds\n\n\n1576.99 USD (GG21)\n", "hidden": false, "question": "Funding Sources and Total prior funding for project in USD", "questionId": 1}, {"type": "number", "answer": "3", "hidden": false, "question": "Team Size", "questionId": 2}, {"type": "paragraph", "answer": "https://www.linkedin.com/in/zaharia-livia-4b1425a0\n\n\nhttps://www.linkedin.com/in/antonkulaga ( https://github.com/antonkulaga )", "hidden": false, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 3}, {"type": "short-answer", "answer": "@ParametricLivia @antonkulaga", "hidden": true, "question": "Telegram username", "questionId": 4}, {"type": "short-answer", "answer": "8 months from the original start with GlucoseDAO in the GG21", "hidden": false, "question": "How old is the project? (Months)", "questionId": 5}, {"type": "paragraph", "answer": "# Problem Statement\n\n### User Challenge:\nCGM users (diabetics and health enthusiasts) manually predict glucose changes from charts, which is exhausting and inefficient.\n\n### Data Issues:\n- Growing CGM adoption generates underutilized data.\n- Most existing data is clinical/academic, not practical for everyday use.\n\n### Model Limitations:\n- Current glucose prediction models are complex, hard to deploy, and lack user-friendliness.\n\n### Ethical Concern:\nPlatforms like [Tidepool](https://www.tidepool.org/bigdata) sell user data to companies without fairly compensating data owners.\n\n### Proposed Solution:\nGlucose DAO’s smart contracts will reward users for their data contributions.\n\n**Learn More**: See our 3-minute introductory video: [Watch the video](https://www.youtube.com/watch?v=AlzdvK6opQc)\n\n---\n\n# Proposed Solution\n\n### Decentralized Database:\nCreate an open, decentralized database of blood glucose values from CGM devices, with volunteers contributing data, including health and lifestyle factors (e.g., diseases, weight, height).\n\n### Machine Learning Models:\n- Train time-series ML models to predict glucose trends and fine-tune them per user for personalized predictions, contributing both models and dataset to the [GlucoBench](https://github.com/IrinaStatsLab/GlucoBench) benchmark.\n\n### Glucose DAO:\nForm a Decentralized Autonomous Organization (DAO) to oversee data collection, model development, and establish protocols for data sharing and smart contracts, ensuring participants are rewarded for commercial use of their data.\n\n---\n\n# Roadmap\n\n### Step 1: Decentralized Database\n- Launch an open database for CGM data collection.\n- Promote via digital channels (e.g., chatbot) to onboard volunteers.\n- Release alpha version for user experimentation.\n\n### Step 2: Machine Learning Models\n- Begin with data preparation (scripting, code adjustments, testing).\n- Train time-series ML models with a slight delay after Step 1.\n- Fine-tune per user for personalized predictions and contribute to GlucoBench.\n\n### Step 3: Glucose DAO\n- Establish the DAO to govern data and development.\n- Develop data-sharing protocols and smart contracts for user rewards.\n- Benchmark human accuracy to refine tools.\n\n**Note**: Steps run in parallel once initiated, with Step 2 slightly delayed for preparatory adjustments.\n\n---\n\n# Timeline\n\n**6-Month Goal**:  \nDevelop and launch starter versions of both the prediction pipeline and the benchmarking game.\n", "hidden": false, "question": "Describe your project in a few paragraphs (ideally including a problem statement, solution, roadmap, and timeline for implementation)", "questionId": 6}, {"type": "paragraph", "answer": "This project aligns with DeSci’s mission by making valuable health data and insights accessible to everyone. It empowers individuals to better understand and manage their health and longevity using clear, actionable information. By openly sharing collected data, it benefits both everyday users and academic researchers, fostering collaboration and scientific progress.\n", "hidden": false, "question": "How does your project support the mission or goals of DeSci?", "questionId": 7}, {"type": "paragraph", "answer": "½ for equipment and resources\n ½ for personnel \n", "hidden": false, "question": "Describe how funds from the grants program will be used for your project.", "questionId": 8}, {"type": "paragraph", "hidden": false, "question": "If you participated in past grant rounds (i.e. DeSci GR15/ Beta/GG 20DeSci round/GG21 DeSci round) using a different project payout wallet address, please share it here.", "questionId": 9}, {"type": "paragraph", "answer": "# UPDATES\n\nPrevious rounds and their updates are mentioned on the project [Karma GAP page](https://gap.karmahq.xyz/project/glucosedao/funding).\n\n---\n\n## Presenting the Project to Communities\n\nSince securing DeSci GG21 funding, we’ve pushed to grow our community, starting with longevity-focused groups, as they often use CGM for health optimizations. First, we showcased a figure on a poster at ARDD (Aging Research and Drug Development), a major aging conference in Copenhagen ([agingpharma.org](https://agingpharma.org/)). Next, we gave a talk at Zelar City in Berlin ([zelar.city](https://www.zelar.city/)), pitching GlucoseDAO and the perks of glucose tracking. Both events doubled as networking hubs to snag supporters.  \nAt Zelar, we also started a trial with 7 non-diabetic volunteers who funded their own CGM sensors to monitor blood sugar swings. We’re still crunching the data, alongside smartwatch metrics they shared. It’s a small batch, but it’s live testing in action.\n\n**Proof of Work:**  \nCheck the talk: [YouTube](https://www.youtube.com/watch?app=desktop&v=uerUE6OOgPI)\n\n---\n\n## Creating the Open-Source Dataset and Codebase\n\nWe promised open-source data, so we started with my personal CGM dataset, cleaned it up, and shipped it. Here’s what’s live:  \n- **Demo App**: Hosted on Hugging Face Spaces at [GlucoseDAO Demo App](https://huggingface.co/spaces/GlucoseDao/glucosedao) — first peek at the GlucoseDAO app.\n- **Dataset**: Processed and open at [Glucose Processed Dataset](https://huggingface.co/datasets/Livia-Zaharia/glucose_processed) — powers the model and the ARDD 2024 figure.\n- **Model**: Gluformer model at [Gluformer Model](https://huggingface.co/Livia-Zaharia/gluformer_models) — drives the app’s first version and the ARDD figure.\n- **Code**: All repositories are at the [Github organization](https://github.com/GlucoseDAO) for anyone who wants to dig in.\n\n**Proof of Work:**  \nAll hosted here: [Hugging Face - GlucoseDAO](https://huggingface.co/GlucoseDao)\n\n---\n\n## Scientific Advisory Board and Collaborations\n\nWe’ve secured a robust scientific advisory board and established key collaborations to advance this project.\n\n### Scientific Advisory Board:\n- **Irina Gaynanova** (University of Michigan, Biostatistics)\n- **Prof. Georg Fullen** (Rostock University)\n- **Renat Sergazinov** (GlucoBench author)\n- **Virgil Ilian** (AI advisor)\n\nThe first two have authored widely recognized resources, including:\n- [Awesome-CGM](https://github.com/IrinaStatsLab/Awesome-CGM): A comprehensive repository of all publicly available continuous glucose monitoring datasets.\n- [GlucoBench](https://github.com/IrinaStatsLab/GlucoBench): A leading benchmarking framework for glucose prediction models, published as a peer-reviewed paper and available as open-source code.\n- [Gluformer](https://github.com/mrsergazinov/gluformer): A state-of-the-art transformer model for glucose forecasting, also published in a peer-reviewed paper and implemented in our work.\n\n### Collaborations:\nWe’re working with the open-source ecosystem at [Longevity Genie](https://longevity-genie.info/), integrating their libraries to streamline development.\n\n---\n\n## Chatbot Development\n\nWe’re developing a chatbot to enhance user access to glucose-related information and promote GlucoseDAO’s mission. Key features include:\n- **Project Inquiries**: Users can ask about GlucoseDAO directly, eliminating the need to scout websites.\n- **Semantic Search**: Extracts insights from indexed scientific papers on glucose management, addressing common user questions like “What’s the latest research?” or “How does Gluformer model work?”\n- **General Q&A**: Answers questions on diabetes, glucose management, and its impacts.\n\n### What Sets It Apart:\nUnlike standard LLMs with generic responses, this chatbot taps into niche, unindexed, or highly specific data, simplifying access to cutting-edge information. For users, it streamlines research; for our team, it aligns with our open-source ethos, spreading knowledge while advancing our cause.\n\n### Development Details:\n- Fully open-sourced at [GitHub - Just-Chat](https://github.com/GlucoseDAO/just-chat), built using the Just-Chat library.\n- We’re currently in the critical testing phase, refining functionality, adding additional papers, tuning prompts, and ensuring stability to prevent crashes.\n\n**Proof of work:**  \n[GlucoseDAO Website](https://glucosedao.org/)\n\n---\n\n# MILESTONES\n\n### Sugar-sugar Prediction Game  \n**Apr 4, 2025 - Jun 30, 2025**  \nDevelopment of a glucose prediction game. Why? Because, at least for the moment, no one knows what is the gold standard in glucose prediction. We know how accurate a model is statistically, however, there is no reference for actual use. How good is a prediction model in comparison to a human, asked to estimate how glucose will go in the next hour? Because that is what we have been doing as diabetics for all our lives. And because we have to set a standard.  \nHow would we know we are doing something that actually helps them? What exactly is it though?  \nIt will be a simple online game — users are given the graph of 8 hours before and asked to predict what will happen next by drawing the curve for 1 hour. Obviously, the graph they receive will be an extract from a continuous series from my data. We think it will be easy to play and will engage the users, also giving us information and spreading the news about the study. The timeline is longer because it is only a very rough sketch as can be seen on the provided visuals.\n\n---\n\n### Gathering a Community  \n**Mar 24, 2025 - Sep 1, 2025**  \nWe started with this idea, and this is a persistent concept. We hope that more people, especially those who need glucose prediction, will hear about us and spread the word, helping those in need. Of course, we also aim to inform people who aren’t directly involved (like the non-diabetic community) about the importance of tracking their glucose.  \nHowever, this is only milestone 3 because, to reach out, we need things that people will engage with, such as the chatbot and the game. It would be nice to claim everything will be just solved, but we have to take it step by step. Therefore, I will leave the end date as far away since community formation is an ongoing process.\n\n---\n\n### Glucose Prediction - First Version  \n**Apr 2, 2025 - Dec 4, 2025**  \nFor those wondering why this is the least priority: it is because we need to solve all the preceding tasks before we can continue. We want to develop a glucose prediction app for 1 hour into the future. It will allow users to upload CSV files from their CGM and gather a rough sketch of what could happen in the future. However, for that, we need to:\n- Find out the types of data from commercially available Continuous Glucose Monitors.\n- Gather data to train and test models (we still have few participants).\n- For prediction to be accurate to a person, we need to determine the best way to train that model and the minimum amount of data needed.\n- Study how heart rate, sleep, activities, and other factors influence it.\n- Actually implement the software.\n- Spread the word about it.\n- And many other obstacles we still don’t know.\n\nAs such, we plan to have the first sample for the general public by December. The timing is due to the amount of work needed, but also to the back processes involved, such as promoting, networking, and all the previous milestones, which are currently handled by the same team of people.\n", "hidden": false, "question": "If this project participated in past DeSci grant rounds, please share any new updates or milestones since the last round.", "questionId": 10}, {"type": "paragraph", "answer": "Regarding previous work and a bit about it’s history- the project started with an attempt to read my own glucose patterns. Not using time series but rather image recognition.It can be seen in the repos such as https://github.com/Livia-Zaharia/glucose_prediction where previous tries were merged.\nAlso since last round we have applied to other several grants but have yet to receive answers.\n", "hidden": true, "question": "Is there any other information you would like to share about your project, previous work, other project affiliations, or potential conflicts of interest? (or anything not covered elsewhere in this application that you feel is relevant to share)", "questionId": 11}, {"type": "dropdown", "answer": "Yes", "hidden": false, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 12}], "project": {"id": "0x2769b17453567e211e8cf281fb88f288166c3d2c0c956d432a1e6dd2812f9b1c", "title": "GlucoseDAO", "logoImg": "bafkreic4gm5icrhdbe5mtld37jv6swmv7fgag6omj55ye4p35yesd5a4gi", "metaPtr": {"pointer": "bafkreic2bbs7leud55imhi3hf7bhuu2gosor65bq7mjmu4whch4e5hsexy", "protocol": "undefined"}, "website": "https://glucosedao.github.io/", "bannerImg": "bafkreibz6nb7fjxh3sjqcatjqyviriac6f2ngmb3uaabl7lmaadqg5scsu", "createdAt": 1723061159601, "userGithub": "Livia-Zaharia", "credentials": {"github": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..RIm0krVWnEH46BmiLhm6qNmwyZrarp7OI4o-KXBVW2x1WYGECkY8tBcNpO7J9AKFhbmEMYtvqAsWKhRj7UiFCQ", "type": "Ed25519Signature2018", "created": "2024-08-07T20:04:28.541Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-08-07T20:04:28.541Z", "expirationDate": "2024-11-05T20:04:28.541Z", "credentialSubject": {"id": "did:pkh:eip155:1:0x178173C996a8D19d60E9BC9d0a84c0c7D4cd5c5b", "hash": "v0.0.0:ZEtLNKyfDdoSewPe4qIGVDqh+WgLd5PBtuLmTfn3Upg=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextGithubOrg#GlucoseDAO#123985032"}}, "twitter": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..ZxeaNI25x-LkbiXSLD0XkZX06QHl5A2NHdK0UqngZ6wMQq5mxl2iJNhebiblOjAhgkKLQTyiKSLZpKCAvr4rAA", "type": "Ed25519Signature2018", "created": "2024-08-07T20:01:56.904Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-08-07T20:01:56.904Z", "expirationDate": "2024-11-05T20:01:56.904Z", "credentialSubject": {"id": "did:pkh:eip155:1:0x178173C996a8D19d60E9BC9d0a84c0c7D4cd5c5b", "hash": "v0.0.0:BpEfL6waCc6+/KWFn7s8Nt6mBKqlBVdJxHA2nyN1p/o=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextTwitter#glucosedao2024"}}}, "description": "# Applying Decentralized Science and Funding to the Glucose Data Issue\n\n## Why It Matters\n\nRecent studies have shown that glucose values are related to health and longevity, making this issue relevant to anyone interested in improving their health and lifespan, not just diabetics.\n\n--------\n[Short 3 min video about GlucoseDAO](https://www.youtube.com/watch?v=AlzdvK6opQc)\n\n\n[Longer video about GlucoseDAO with studies](https://www.youtube.com/watch?v=AlzdvK6opQc)\n\n-------\n\n## Glucose Values and Their Significance\n\nGlucose levels and dynamics impact overall health and longevity, however, there are three major groups that stand out:\n\n1. *Individuals with medical conditions*: This includes those with type I and II diabetes, pre-diabetic individuals, pregnancy diabetes, etc., comprising 460 million people worldwide.\n2. *Healthy lifestyle pursuers*\n3. *Longevity enthusiasts*\n\n## A Personalized Approach\n\nUnderstanding your glucose values and dynamics is about self-knowledge. Each person's glucose response is unique, and continuous monitoring of changes is more informative than single-point tests. Comprehensive study and data collection are necessary to understand how these fluctuations affect individuals and how to improve them.\n\n- For people with diabetes, it allows maintaining normal ranges and avoiding hypo and hyperglycemia.\n- For health and longevity seekers, it can help optimize diet, lifestyle, and reduce damage from glucose spikes and risks of prediabetes.\n\n## Proposed Solution\n\nWe propose:\n\n1. *Establish a decentralized open database of blood glucose values* from Continuous Glucose Monitoring (CGM) devices. The data will be contributed by volunteers and may also include relevant health and lifestyle-related variables (diseases, weight, height, etc.).\n2. *Train models to predict blood glucose trends* and fine-tune existing public models on this data. Contribute the model and dataset to [GlucoBench benchmark](https://github.com/IrinaStatsLab/GlucoBench/).\n3. *Establish a Glucose DAO (Decentralized Autonomous Organization)* which will govern data collection and development and will also work on protocols of sharing glucose data and smart contracts that will allow participants to be rewarded in case of commercial applications.\n\nThis approach aligns with recent research on privacy-preserved blood glucose level cross-prediction using decentralized federated learning. (https://github.com/ChengzhePiao/coldstartbglp)\n\n## Examining the Current Use of Glucose Data\n\nA clear need for a DAO approach is evident. This strategy addresses the prevalent issue of user data being sold without revenue sharing. A DAO structure offers two potential solutions:\n\n### Open-source for Research\n\nA DAO can facilitate an open-source database of anonymized glucose data, promoting transparency and collaborative research while maintaining privacy of contributors. This aligns with the decentralization principle of DAOs, ensuring no single entity has total control over the data.\n\n### Fair profit-sharing for commercial use-cases\n\nFor commercial applications, DAO can develop smart contracts to automate profit-sharing with data contributors to ensure fair compensation.\n\nBy implementing a DAO, the glucose data ecosystem can evolve from its current centralized model to a more transparent, collaborative, and user-centric approach.\n\n## Benefits\n\nThis project aims to democratize access to valuable health data and insights, empowering individuals to make informed decisions about their health and longevity. By supporting this project, you are not only helping people with medical conditions but also paving the way for access to information that could potentially extend or at least improve your lifespan.\n\n## Additional Information\n\n\n### Team Size\n\n- 3\n\n### Problem Statement\n\n- *Underutilized data from increasing CGM adoption*: 463 million people have diabetes worldwide and millions of them already use CGMs for many years. Existing datasets have only dozens of people monitored at short intervals of several months [Renat Sergazinov, et. al, \"GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks.\" OpenReview, 2023, see also https://github.com/IrinaStatsLab/Awesome-CGM). While acknowledging the endeavor of Irina Gaynanova lab of summarizing existing datasets, we believe that a novel, large longitudinal dataset must be created.\n- *Most existing data are of clinical/academic nature*: While useful for research and development, most existing datasets are usually of academic/clinical nature, have short timespans, have a small number of non-diabetic people, and do not include some important aspects (like data on sensor resets, for example).\n- *Existing models are hard to deploy and use in real-world scenarios and are not user-friendly*: Only a small part of the models is public and most of them are hard to deploy and use on an everyday basis for an average user. What is important for the end-user is that the model pre-trained on public datasets should be fine-tuned on your own data to predict your future values and your risks.\n- *Existing data sharing platforms* (like https://www.tidepool.org/bigdata) sell users' personal information to companies without compensating the data owners. We believe users should be fairly rewarded when their data is sold for commercial purposes. One potential solution is the development of smart contracts by the Glucose DAO.\n\n### Importance\n\nGlucose prediction is crucial for longevity, as both levels and variations impact health. Studies show high glucose values and variations negatively affect longevity in the general population, not just diabetics. The relationship between glycation, Advanced Glycation End Products (AGEs), and health outcomes further underscores this importance.\n\nFor reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643203/\n\n### Personal Motivation\nLivia Zaharia- team member and diabetic type 1 since 2001:\n\nAs a diabetic with extensive CGM data, I've recognized the need for a pattern-reading model to predict future glucose values. This project aims to bridge the gap in long-term data availability from non-diabetic individuals and provide valuable insights for glucose management and overall health optimization.", "lastUpdated": 0, "projectGithub": "GlucoseDAO", "projectTwitter": "glucosedao2024"}, "recipient": "0x00763d584dc079DBFF7d9581B2f632b183323Dca"}}