For over two decades, I have worked to harness big data to uncover the root causes of chronic diseases, such as cancers. I also worked to create models that can guide individuals, physicians, and policy makers to develop strategies for prevention and stop diseases from happening in the first place.
Image credit: CHRIS HARTLOVE
My lab and I have unveiled genetic variants linked to multiple cancers, illuminated the complex interplay between genes and the environment in cancer risk, identified proteomic biomarkers with potential as drug targets for disease prevention, and developed predictive models for assessing individual risks of cancers and other chronic diseases by integrating genetic and environmental factors.
The statistical methods, software, and data resources we have created have been widely adopted by researchers worldwide, had have helped advance similar investigations across too many diseases to name.
With consistent support from NIH and other federal health agencies, I have built a nimble yet highly productive lab, trying to maintain the critical size needed to spark collaboration and innovation, but without becoming unwieldy. However the looming threat of budget cuts to these agencies jeopardizes my ability to sustain this critical momentum.
A reduction in funding would erode the competitive edge my group has built over decades and will ultimately impact our capacity to make a difference in helping disease prevention in both individual—and population—levels.
Guided by the principle that an ounce of prevention is better than a pound of cure, I have been poised to accelerate progress in my mission, particularly with recent advancements in AI technologies. And I have proudly recruited and trained some of the brightest PhD students, post-doctoral fellows, and undergraduates, instilling in them a passion for applying statistical and computational methods to tackle the chronic disease crisis.
The United States is grappling with a national crisis due to the immense health burden and skyrocketing costs associated with chronic diseases. A recent White House Executive Order underscores the severity of this issue, stating that "90% of the nation's $4.5 trillion in annual healthcare expenditures is for people with chronic and mental health conditions." It calls for urgent action to "understand and drastically lower chronic disease rates and end childhood chronic disease."
The work of my colleagues and I in the fields of biostatistics, genetics, and epidemiology has laid a solid foundation for advancing disease prevention research in alignment with the White House's vision. Now is the time to amplify this investment, leveraging the transformative potential of big data and of artificial intelligence to accelerate progress.
But budget cuts at this critical juncture would severely undermine our ability to build the large-scale databases necessary for discovery, develop the computational and methodological infrastructure required for innovation, and, most importantly, train the next generation of scientists poised to fulfill the promise of this field.
These setbacks would delay progress in addressing the chronic disease crisis, and will lead to an enormous lost opportunity to improve the health, economy, and overall security for the U.S.
Nilanjan Chatterjee is the Bloomberg Distinguished Professor of Biostatistics and Genetic Epidemiology at Johns Hopkins University, with appointments in the Department of Biostatistics at the Bloomberg School of Public Health and the Department of Oncology at the School of Medicine. He leads a broad research program in quantitative research that cuts across multiple areas of modern population-based biomedical science including statistical genetics/genomics, precision medicine, and big data. The scientific goals of his studies include discovery of new biomarkers, understanding disease mechanisms, characterizing disease risk and developing risk-stratified approaches to disease prevention.
Posted in Science+Technology, Voices+Opinion
Tagged nih funding, biostatistics, bloomberg distinguished professors
