We’ll get to the microarrays in a moment. But first…

It’s Thursday and the latest issue of The New England Journal of Medicine has arrived in my mail. The article that’s getting the most play in the popular press is “The value of medical spending in the United states, 1960-2000.” According to authors David Cutler, Allison Rosen, and Sandeep Vijan, from 1960 through 2000, the life expectancy of a newborn in the USA increased by 6.97 years and lifetime medical spending adjusted for inflation increased by approximately $69,000. That’s an increase, not a total!

The authors use this data to calculate the cost per year of life gained. The numbers are startling. The average cost per year of life gained for a 15-year-old during the period 1960-2000 was more than $31,000. For a 65-year-old, the cost per year of life gained was more than $84,000. Between 1990 and 2000, the costs per year of life gained for a 65-year-old was $145,000!

The authors make a big assumption: that at least half of the gain in life expectancy is due to medical advances, specifically decreases in infant mortality and decreased death rates from cardiovascular disease. Among their conclusions is that the costs of medical care for the elderly are rising more rapidly than did any gains in life expectancy.

That’s not a new conclusion. I recall hearing somewhere that about half of all Medicare funds go to the care of an individual during the last year of his or her life. If someone can give me a cite (or dispute it), feel free.

Reading on in the same issue of the NEJM, I came across a letter commenting on the article Microarray analysis and tumor classification that was published back in the June 8, 2006 issue. That very same article has been sitting in my ever-enlarging pile of things to read. Some of the material at the bottom of the pile is surely out-of-date by now.

Spurred on by the letter, I sat down and read the article. It is a good summary of the techniques being used to identify and interpret patterns of gene expression in cancer cells. There are nice illustrations of microarray analysis and the development of a gene expression matrix. It seems very likely that these techniques will one day augment or replace TNM and other staging systems in choosing therapies or estimating prognosis.

The authors also include a nice summary of all the “-omics” (genomics, metabolomics, proteomics) that are finding their way into the medical literature.

Among the authors’ conclusions are that

“…gene-expression signatures obtained with the use of microarray analysis are difficult to interpret with respect to the biology of the underlying disease. Ultimately, finding genes that can be linked through their mechanism to disease outcome suggest potential therapeutic interventions. But failure to provide a biological interpretation does not diminish the potential clinical usefulness of well-established biomarkers. Many biomarkers, such as prostate-specific antigen and caricinoembryonic antigen, that have unknown functions are useful as diagnostic or prognostic markers for various diseases. It may be useful to consider the lists of genes emerging from classification experiments as sets of biomarkers; insight into biologic mechanism is a bonus.

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