The transition from data scientist to patient–scientist has given me new perspectives into clinical research and strengthened my commitment to open science. Although limitations on data availability have led to frustration, collaboration bodes well for a future in which patients will have access to more personalized information.

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Being diagnosed with an incurable illness is a shock, even more so if you have no symptoms. In my case, I also felt confused to learn that I had a disease — IgA nephropathy (IgAN) — that I had never heard of. For many years before my diagnosis, my research had focused on the molecular basis of diseases, and I felt that I was reasonably familiar with a variety of medical conditions. Following my diagnosis, I did what I expect most people do when they want to learn about a disease: I started to read.

As I read, I wondered how much I wanted to know — whether I would become obsessed with my condition if I read too much. On the other hand, the scientist in me kept asking questions about the disease pathophysiology. I decided to dive in. I realized that we could leverage the expertise of my group in computational biomedicine to provide insights into the pathogenic mechanisms of IgAN and, who knows, ultimately better treat my own condition. I also noted that it is substantially more difficult to get funding for kidney diseases than other conditions, and thus research in my field of ‘big data’ lagged in nephrology compared to other disease areas. This felt unbalanced given the high prevalence and mortality of kidney diseases.

My engagement with nephrology as a patient–scientist led me to examine clinical data more closely — and differently — than I had as a researcher. As just one example, Kaplan–Meier curves help researchers to understand the effect of a disease or treatment on populations. However, as a patient, I want to know where I am on that curve. How can any individual interpret and use these data as a basis to potentially make profound decisions in their life — if they know that they have, for example, a 30% chance of reaching kidney failure in 20 years? How should they ‘re-evaluate’ their odds if their last biopsy was 10 years ago? As a patient, I would like to have access to more precise estimates, for example, through a ‘digital twin’, to facilitate data-driven decision-making specific to my situation.

Uncertainty also applies to the choice of treatment and the decision to enrol in clinical trials. Trial participation has the twofold value of enabling early access to a potentially beneficial treatment and offering the opportunity to contribute to the success of new therapies. However, participation is not without risks, and the decision to participate is ultimately a personal one that is based on limited data.

Throughout my career, I have been an ardent practitioner of open science, freely sharing our algorithms and results. I deeply value how my institution, the European Bioinformatics Institute (EMBL-EBI), serves as a beacon for open science. My newfound perspective as a patient has reinforced my belief in the value of open data. Data sharing not only aids reproducibility but also enables scientists to extract more information from existing data, thereby accelerating the discovery of disease mechanisms, biomarkers and treatments, and thus providing better value for research funding. Egoistically, I would certainly like that. In reality, however, clinical data are often not accessible to researchers. I have seen with frustration how access requests are sometimes refused. Such refusals are frequently justified by explanations around data protection — a very valid argument, but one for which solutions often exist. I felt that access was sometimes restricted to maximize personal benefit. That stance certainly contradicts my principles as a researcher and my hopes as a patient. I expect that most patients would want their data used in a way that will provide maximal benefit to them and other patients.

Through collaboration and listening to the views of patients, nephrology is moving forward. One example of this approach is the Kidney Precision Medicine Project (KPMP), a collaborative effort in which data-sharing with the community is a mandate that is taken very seriously by the leadership. Furthermore, patients are key drivers of the project, and their testimonies serve as a reminder that the true goal is to help those with kidney disease, not to advance careers. Inspired by KPMP, we have launched the IgAN Atlas, an open and collaborative initiative comprising an international team of scientists, clinicians and patients that aims to build a molecular atlas to accelerate research and ultimately enhance clinical practice through the use of digital twins. Given the heterogeneity of disease progression, the diversity of treatments under development and their substantial cost, we hope that the IgAN Atlas will help to identify biomarkers that lead to improved patient stratification and better patient outcomes.

Becoming a patient has given me a new perspective on biomedical research. Despite my background in data analytics, I have found that I do not necessarily have access to the data or information I need to fully understand my own prognosis or treatment options. It has also reinforced my view that data, expertise and knowledge must be shared across the community to maximize the speed of scientific discovery, and extended that view to the need to give information to patients in a form that is as personalized as possible, to enable patients to make more informed decisions. Finally, it is clear to me that patients must be involved across all stages of research planning to ensure that the true purpose of nephrology — to help those with kidney diseases — is not forgotten.