Kristin Leonberg

Final Presentation

Final Project

Integrating Diet Insights with Machine Learning and Digital Twins for Enhanced Kidney Disease Progression Prediction

Project Description

Ultra-processed foods (UPF) are formulations of ingredients that are mostly of exclusive industrial use and may contain additives like artificial colors, flavors, or stabilizers. They are also characterized by their industrial ingredients and additives such as excess sodium and artificial preservatives, pose significant health risks. The sale and consumption of UPF is increasing despite their associations with increased risk for several non-communicable diseases including chronic kidney disease (CKD). The Nova classification system, with its broad and exclusive categories, often fails to capture the intricate details of food processing. Additives commonly used in UPF are difficult to identify and include excess sodium, phosphates, and artificial preservatives, which pose heightened risks for individuals with compromised kidney function, exacerbating the burden on already weakened kidneys and potentially accelerating disease progression. This study aims to refine the Nova classification system for UPFs by integrating advanced machine learning techniques to analyze detailed ingredient specifications. Through the creation of a dynamic dashboard, we seek to distinguish between nutrient-dense and calorically dense UPFs.

We would leverage these insights to develop precise, personalized dietary recommendations aimed at mitigating the progression of CKD, while also influencing policy for clearer food labeling and setting safer limits on potentially harmful additives. We will employ machine learning and predictive analytics to project future states from historical data and explore the effects of dietary modifications. In developing AI for kidney disease, it is crucial to integrate principles of beneficence and non-maleficence to ensure that the technology does no harm while maximizing benefits; fairness and equity to guarantee unbiased treatment across diverse populations; transparency and accountability to maintain public trust; informed consent and privacy to protect patient data; human-centric design to enhance user interaction; cross-cultural sensitivity to address global healthcare needs; risk management and mitigation to prevent potential adverse outcomes; and ethical decision-making frameworks to guide all stages of AI development and implementation. Digital twins in nutrition research offer a transformative tool for personalizing dietary strategies, enabling researchers to simulate and analyze the specific effects of dietary changes on individual health outcomes. This technology allows for precise adjustment of nutritional plans to optimize health benefits and manage conditions such as chronic diseases more effectively.

In our study, digital twins will allow for the simulation of an individual’s physiological responses to different levels and categories of UPF. By modeling how specific nutrient additives and diet modifications impact kidney function over time, continuous data collection and analysis provide invaluable insights into the long-term impacts of dietary habits, enabling adjustments in dietary recommendations based on real-time data while requiring a long-term collection of health and nutrition information. Therefore, to develop an effective model for creating digital twins, we would need access to specific, frequently updated, and potentially confidential data on food formulations as well as frequently updated longitudinal health information. The anticipated impact of using AI predictions would create a much deeper understanding of the diet/disease relationship while tailoring dietary interventions based on personal health data. The culmination of this project includes a scalable nutrition platform that assists both individuals and clinicians in assessing UPF quality and making informed dietary choices based on detailed nutrient profiles slow disease progression and potentially improve clinical decision making.

In summary, this project aims to enhance the Nova classification system for UPFs using advanced machine learning to analyze detailed ingredient specifications and identify the impact on CKD progression. By creating a dynamic dashboard, the study will differentiate between nutrient-dense and calorically dense UPFs, providing tailored dietary recommendations to mitigate CKD progression. Additionally, the project has the potential to influence policy on food labeling and safety standards for additives. Ethical AI principles and digital twins will be utilized to simulate individual responses to dietary changes, enabling real-time adjustment of dietary guidelines based on continuous data analysis. This comprehensive approach seeks to improve dietary decision-making and enhance patient outcomes through a scalable nutrition platform.

Figure 1: Concept Diagram showing the relationship between kidney disease, risk factors and diet.

Figure 2: Causal Diagram showing the complex relationship between diet and chronic kidney disease including both modifiable and non-modifiable risk factors, confounding factors, and potential interactions