Nephrotic syndrome: a non-specific classification

Nephrotic syndrome is a catch-all term for a collection of symptoms. These include protein in the urine and fluid retention. It can affect people of any age but is usually first diagnosed in children under 5 years old. This condition can have many different types: some patients have genetic nephrotic syndrome while others have nephrotic syndrome with no known genetic cause. Some patients respond to steroids while others don’t. Still others can be steroid responsive only to later become steroid resistant. Time is wasted exposing patients to possibly futile treatments while trying to figure out which type the patient has.  

OK computer

We are witnessing a boom in something called “Big Data”. With support from Kidney Research UK, scientists at Bristol Renal, under the leadership of Professor Moin Saleem, have already established a huge database of information by recruiting 800 patients. Along with partnerships with similar databases in India, Sri Lanka and South Africa, Moin has access to an overwhelming amount of data. The answer is in there but finding it by traditional means would be extremely difficult.

Moin will be exploiting recent advances in machine learning, using computers to sift the data and identify useful ways to tell patients apart based on what is causing their particular form of the disease. 

What this might mean for kidney patients

Quite simply, this work could lead to much faster, more accurate diagnoses with better targeted treatments. By identifying the very subtle differences between patients, Moin’s work will take us closer to personalised medicine: treatment plans almost directly tailored to the individual.

Collaboration with partners in India, Sri Lanka and South Africa will allow for much more ethnic diversity. This means differences that might have been missed in a UK-only cohort could be found. 

“With funding from Kidney Research UK, we now have capability to enhance our ability to interrogate patient datasets that contain vast amounts of biological information about each individual. The project addresses a crucial need when these datasets are made available, to sift out the key signals that tell us what drives the patient’s kidney disease. The award will be used to grow these expert capabilities within our group, and allow us to move ahead with skills that can be applied to many difference kidney conditions.”