Big Data for Personalized Diets

A recent commentary in the NY Times discusses the future of personalized diets.  Along the way, the commentary notes how little we know about the best diet and how difficult nutrition research is to complete.

The A.I. Diet by Eric Topol who is the author of the forthcoming “Deep Medicine,” from which this essay is adapted

An excerpt:

It turns out, despite decades of diet fads and government-issued food pyramids, we know surprisingly little about the science of nutrition. It is very hard to do high-quality randomized trials: They require people to adhere to a diet for years before there can be any assessment of significant health outcomes…

Meanwhile, the field has been undermined by the food industry, which tries to exert influence over the research it funds.

Now the central flaw in the whole premise is becoming clear: the idea that there is one optimal diet for all people…

Only recently, with the ability to analyze large data sets using artificial intelligence, have we learned how simplistic and naïve the assumption of a universal diet is. It is both biologically and physiologically implausible: It contradicts the remarkable heterogeneity of human metabolism, microbiome and environment, to name just a few of the dimensions that make each of us unique. A good diet, it turns out, has to be individualized.

My take: Dr. Topol makes some important observations and he is right that there is not a simple diet solution for everyone.  Nevertheless, in the near future, personalized medicine is not coming to our dinner tables and we have to rely on what we know right now –don’t eat too much sugar, do eat more fruits and vegetables, and don’t eat too much.

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The Narrow Path of Personalized Cancer Medicine

Since I’m not directly involved in oncology care, I have a limited perspective on how quickly molecular medicine may transform cancer care.  A recent commentary (IF Tannock, JA Hickman. NEJM 2016; 1289-94) explains the “Limits to Personalized Cancer Medicine.”

While the idea of careful molecular characterization of tumors that lead to targeted therapy with better survival and better patient quality of life has been proven effective in several circumstances, there are a number of reasons why this approach will not be useful for most cancers.

Key points:

  • Examples of current personalized cancer Rx: trastuzumab for HER2-expressing breast cancer and vemurafenib for BRAF-mutated-expressing melanomas.
  • Very few studies have shown feasibility/effectiveness of targeted drug treatment
  • There has been limited success with targeted drugs within and outside studies
  • Though proponents of targeted therapy expect further advances, tumors typically have heterogeneity which allows a Darwinian evolution to evade these new therapies. “Cancer cells have an almost universal capacity to develop resistance to a single molecular targeted agent by means of upregulation of the partially inhibited pathway, mutation of the target, or activation of alternative pathways.”
  • Targeted therapies are usually limited by only partial inhibition of the signaling pathways and by toxicity when used in combination therapy.
  • In some cases, a clonal driver mutation may be present which would be present in all cell lines –however the authors note that success from this approach is likely to be rare.
  • Cost: “new drugs to treat cancer are marketed at ever-increasing prices…unrelated to value (i.e. to clinical effectiveness)….but the development and marketing of expensive drugs with marginal effectiveness diverts resources from the development of more effective therapies.”

My take (borrowed from authors): “The concept of personalized medicine is so appealing…[but] there should also be a clear message to patients that personalized cancer medicine has not led to gains in survival…and is an appropriate strategy only within well-designed clinical trials.”

Related blog post:

University of Virginia

The Lawn, University of Virginia