Fitbit needed for 7,000 year-old caveman

February 13, 2014 | Posted in Latest News

La Brana man

Back in 2006, a group of mountaineers climbing in north-west Spain stumbled upon the body of an ancient human man. Carbon dating put La Brana man – named after the cave in which he was discovered – at having lived about 7,000 years ago. He died in his early thirties and would have lived around the Cantabrian mountain region.

La Brana man was dark-skinned, had black or dark brown hair, yet he had jarringly blue eyes. He was also likely lactose intolerant, so you wouldn’t have wanted to be around him after a tub of Ben & Jerry’s. We know such specific details about a person who was quite literally a ‘caveman’ because fragments of his DNA were well-preserved by the cool, dry atmosphere of the cave, and researchers have been able to piece together his genome. Such is the power of DNA.

One mantra of the Quantified Self movement is the pursuit of improvement through self-knowledge – using the personal data collected through self-tracking to help improve overall well-being. And if you believe the hype of Big Data (and let’s be honest….we all do) these enormous datasets will soon be mined to unravel the secrets of what makes us tick and the actions we can take to improve our health and longevity. This is one of the ambitious aims which Quantid has in its crosshairs (click here to support our campaign).

Self-tracking revolves around the personal data we generate as we go about our day-to-day lives: the amount we sleep, the distance run, our weight and blood glucose levels. But to extract powerful correlations and trends from this quantified personal data and identify actionable behaviour change, we must also know the fundamental aspects of who we are. Like La Brana man, data on our ethnicity, gender, physical traits and susceptibility to various diseases and medical conditions are essentials. Two separate individuals might show very similar patterns in their self-tracking data, but if one is a Caucasian female with diabetes while the other is an Asian male with a nut allergy, the recommendations for health improvements might be widely different.

The obvious way to collect information on physiological traits is self-reporting. We’ve all filled out surveys that have asked about our ethnicity, health status, and the medical conditions we suffer from. But self-reporting is subjective. Ethnicity is a good example: just because someone identifies as Caucasian, doesn’t mean their ancestry is 100% white. My 23andMe results show that I’m 51% European, 42% African and 2% Native American. You can see how inaccurate it’d be were I to self-report as purely ‘White’, ‘Black’ or ‘Asian’.

Furthermore, none of us can’t report on our susceptibility to various medical conditions until these conditions actually manifest themselves, or are revealed through specific tests. A predisposition to obesity or high cholesterol will probably go unnoticed until our bathroom scale is continually delivering bad news, or our blood tests report persistently high LDL.

All our physiological traits are contained within each of individual genomes and I think incorporating DNA information is one of the most exciting aspects of the quantified-self phenomenon. Armed with our complete genome, the analytics engines which are set loose on our personal datasets will have access to awesomely granular information on our gender, race, anatomy, disease susceptibility and much more. And as future research forces the genome to reveal even more of its secrets, these analytic functions will become ever more powerful.

It’s an enjoyable pastime of tech enthusiasts to make extravagant predictions about our technological future, never seeming to notice that our flying cars have not yet materialized. But I think it’s hard to overstate the impact which the marriage of genetic information and quantified data like sleep, mood and activity, will have on driving us towards longer, more healthy and more happy lives. So we’ve got hold of La Brana man’s DNA, now all we need is to find his fitbit.

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