Cristian Pattaro is a researcher in Biostatistics and Genetic Epidemiology at the Eurac Research Institute for Biomedicine, in Bolzano, Italy. He received a MSc degree in Statistics and Economical Sciences at the University of Padova in 1999 and a PhD in Biomedical Statistics at the University of Milano in 2006. He grew his expertise in epidemiology at the Unit of Epidemiology & Medical Statistics of the University of Verona and deepened his understanding of statistical genetics at the Johns Hopkins School of Public Health. At the Eurac Research Institute for Biomedicine he is group leader of the Biostatistics & Epidemiology group and principal investigator of the Cooperative Health Research in South Tyrol (CHRIS) study, one of the largest population-based studies in the country. Together with Anna Köttgen, he is coordinating the Chronic Kidney Disease Genetics (CKDGen) consortium, a worldwide initiative that involves more than one hundred studies and a million study participants aimed at uncovering the genetic underpinnings of kidney function and disease. For several years, he has been teaching genetics of complex diseases at the Vita-Salute San Raffaele University in Milano. His main publication record is in the field of genetic epidemiology of kidney disease. His main research interests are the genetic epidemiology of kidney and cardiovascular health, linkage disequilibrium, and statistical issues in population-based studies with relatedness structures.
How did you get into the CKD field and why is the field important to you?
I got into the CKD field because of the fortunate meeting with a great nephrologist, Dr. P. Riegler at the Hospital of Bolzano, who introduced me into the field. Soon, I became passionate of this topic because kidney disease is amongst the most complex chronic diseases, has a huge impact on human health, and it is extremely challenging from a statistical modeling point of view.
What is a key question in the CKD field now? Where do you think the field is heading?
There are many big open questions in chronic kidney disease research. An important one is: what are the molecular mechanisms we can target to prevent or delay kidney function decline?
What advice do you have for early career scientists that who want to enter the CKD field?
The statistical modeling of complex diseases requires an excellent command of statistical methods but also a clear understanding of the disease characteristics. So, my suggestion to early career scientists who aim at working in the genetic epidemiology of CKD is to start from the clinic. Go and see patients first. Talk to clinicians. Get passionate about the big, unsolved questions for this disease. Then, go back to the data and put your passion on it.