So, for a retrospective approach with existing data, I could try to find a constellation of proxy variables in the ICD9 V-codes and maybe some lab values suggestive of basically healthy patients who consume a lower-than-typical amount of calories. Not in a very health-conscious part of the country though, so unlikely that a large number of patients would do this on purpose, let alone one specific fasting strategy.

Now, something I could do is team up with a local dietician or endocrinologist and recruit patients to try calorie restriction.

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[anonymous]5y0

Here is what seems like a pretty good overview of intermittent fasting: http://easacademy.org/trainer-resources/article/intermittent-fasting

0Lumifer5yUm, calorie restriction in the necessary amounts is quite unpleasant and are you willing to commit to a multi-decade trial anyway..?
3gwern5yWhy not run a pilot on yourself first? The nice thing about IF is that in many forms, it's dead easy: you eat nothing one day, twice as much the next. Record some data on yourself for a few months (weight? blood glucose*? a full blood panel?), and you'll have solid data about your own reactions to IF and a better idea what to look for. Personally, I would be surprised if you could do worthwhile research on IF by mining research records: 'eating food every day' is nigh-universal, and most datasets are concerned entirely with what people eat, not when. You might have to get creative and do something like look for natural experiments involving fasting such as Ramadan. * and don't write off blood glucose as too painful or messy for non-diabetics to measure! Blood glucose strip testing turns out to be easier than I thought. I used up a package recently: while I nearly fainted the first time as my heart-rate plunged into the mid-50s because of my blood phobia, over the course of 10 strips I progressed to not minding and my heart-rate hardly budging.

Request for suggestions: ageing and data-mining

by bokov 1 min read24th Nov 201448 comments

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Imagine you had the following at your disposal:

  • A Ph.D. in a biological science, with a fair amount of reading and wet-lab work under your belt on the topic of aging and longevity (but in hindsight, nothing that turned out to leverage any real mechanistic insights into aging).
  • A M.S. in statistics. Sadly, the non-Bayesian kind for the most part, but along the way acquired the meta-skills necessary to read and understand most quantitative papers with life-science applications.
  • Love of programming and data, the ability to learn most new computer languages in a couple of weeks, and at least 8 years spent hacking R code.
  • Research access to large amounts of anonymized patient data.
  • Optimistically, two decades remaining in which to make it all count.

Imagine that your goal were to slow or prevent biological aging...

  1. What would be the specific questions you would try to tackle first?
  2. What additional skills would you add to your toolkit?
  3. How would you allocate your limited time between the research questions in #1 and the acquisition of new skills in #2?

Thanks for your input.


Update

I thank everyone for their input and apologize for how long it has taken me to post an update.

I met with Aubrey de Grey and he recommended using the anonymized patient data to look for novel uses for already-prescribed drugs. He also suggested I do a comparison of existing longitudinal studies (e.g. Framingham) and the equivalent data elements from our data warehouse. I asked him that if he runs into any researchers with promising theories or methods but for a massive human dataset to test them on, to send them my way.

My original question was a bit to broad in retrospect: I should have focused more on how to best leverage the capabilities my project already has in place rather than a more general "what should I do with myself" kind of appeal. On the other hand, at the time I might have been less confident about the project's success than I am now. Though the conversation immediately went off into prospective experiments rather than analyzing existing data, there were some great ideas there that may yet become practical to implement.

At any rate, a lot of this has been overcome by events. In the last six months I realized that before we even get to the bifurcation point between longevity and other research areas, there are a crapload of technical, logistical, and organizational problems to solve. I no longer have any doubt that these real problems are worth solving, my team is well positioned to solve many of them, and the solutions will significantly accelerate research in many areas including longevity. We have institutional support, we have a credible revenue stream, and no shortage of promising directions to pursue. The limiting factor now is people-hours. So, we are recruiting.

Thanks again to everyone for their feedback.

 

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