Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder and the fourth leading cause of renal failure in the United States. The American Society of Nephrology states that the annual economic burden of ESRD is $32 billion. Patients with end-stage kidney disease (ESKD) spend $14,399 each month on all healthcare services. Promoting healthy eating habits and lifestyle practices could reduce health system costs and enhance patient’s quality of life.
Ketogenic dietary interventions are high-fat, low-carbohydrate and moderate-protein diets which mimic a fasting state. There is some preclinical evidence supporting the hypothesis that ketogenic diet might help to slow the progression of the disease (slowed cyst growth) and reduce the risk of ESKD.
The aim of this project is to integrate an IoT infrastructure with clinical, imaging data and genetic background to guide a tailored nutritional (e.g., ketogenic diet) and pharmacological intervention (e.g., metformin vs tolvaptan) for improving patient care in relation to the risk of disease progression.
Using a wearable device we will monitor patients' eating habits, provide an accurate estimate of the quality of diet and caloric/protein intake and analyse the impact of these interventions on kidney function decline and clinical outcomes in patients with ADPKD.
Multiple Myeloma (MM) is the second most common blood cancer after non-Hodgkin lymphoma. MM is typically preceded by Monoclonal Gammopathy of Undetermined Significance (MGUS) and/or Smoldering Multiple Myeloma (SMM). The risk of progression from MGUS to MM is approximately 1% per year, while the risk of progression from SMM to MM is about 10% per year during the first 5 years, 3% per year over the following 5 years, and 1% annually thereafter. Although various risk stratification methods for disease progression have been validated and employed, new tools are needed to better analyze the complexity of the disease and advance personalized medicine for MM.
The project "Data mining, artificial intelligence, and machine learning approaches to identify subnetworks of cancer associated with early prediction, survival, metastasis or phenotypes in cancer subtypes focusing on myeloma", led by the research group of Professor Angelo Vacca and Professor Roberto Ria at the University of Bari (WP3, Task 3.2c), aims to develop prognostic and predictive models for risk of progression. This will be achieved by collecting and analyzing clinical data using Machine Learning techniques, enabling a more personalized approach to the management of MM patients.
This research was co-funded by the Italian Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, "DARE - DigitAl lifelong pRevEntion" initiative, code PNC0000002
