People use Facebook, Twitter, or other social media sites as channels for self-expression. But whether updating or uploading, people are telling their social stories with only two tools: text and images.

But what if social media wasn’t confined to words and pictures, but instead, allowed users to uploaded graphs or tables? In other words, could data, pure data, become a token in our social currency?

That’s the thought contributed during a panel session at the Health 2.0 Conference in San Francisco by Gary Wolf, contributing editor at Wired, and an organizer of Quantified Self, a community whose users meticulously track certain aspects of their lives, some down to infinitesimal levels, such as how they spend every minute of the day (no joke).

Wolf’s comment followed a presentation by Stead Burwell, the CEO of Alliance Health Networks, who demoed Diabetic Connect an information and community site for patients battling diabetes. Alliance spent a great deal of time (read: money) on creating user profiles that would allow visitors of the site to connect with their peers, patients who share similar experiences. But that connection, they found, was key. As Burwell said in his presentation, users not only like to receive badges and virtual rewards, they like to hand them out as well.

Noting how willingly people update their status on social media sites like Facebook, sometimes with unrestrained detail, Burwell wondered how to bottle this social energy to get patients to openly share personal health data.

In my opinion, the limitations aren’t technical. After all there is nothing preventing users on Facebook from uploading a JPEG charting the number of miles they ran in a given month. Sure, social media sites could make tools available to users to facilitate the process, but that’s the easy part – there are already a number of product-related sites, such as Nike+, that do just this. The shift that Wolf describes, and that Burwell hopes for, is more philosophical, a change in the type of information we feel comfortable sharing with our friends, families, and colleagues.

So here’s my request: If you track any aspect of your life, whether your weekly running mileage, calories consumed by food, weight fluctuations, or daily blood glucose readings, share your data with your social network. Let’s see what happens.

Photo via Flickr / Sean MacEntee

The idea that climate change is linked to the spread of a disease is not new. Some bacteria and viruses, after all, piggyback on an animal or insect, and the infectious advance depends on the host’s reaction to climbing temperatures. Consider dengue, a disease once anchored to tropical climates by its host’s penchant for heat and humidity, which is now pushing further north with its mosquito transits as the upper latitudes get warmer. But according to a study published this past June in PNAS, it’s not only climbing temperatures that are worrisome; in the past, even heavy rains have altered the course of disease, though often in divergent directions.

During the third plague pandemic (China, 1850-1964), researchers found that, for better or worse, the seasonal rains were a strong predictor of how the disease spread. There, storms governed Pestilence’s toll, prodding the disease in the arid north, and quelling it in the humid south.

Rats are the primary host for the bubonic plague, and in general, the more that infected rats move, the more the disease will spread. In the dry north, they figure, the rains quenched the parched landscape, causing the rats, and the disease, to stir. In the southern part of the country, the rains only served to make the humidity worse, perhaps forcing the rats to sit tight.

Keeping tabs on the spread of infectious disease is one thing; understanding the interaction of pathogens, hosts, and behavior is yet another.

Photo via Flickr / Yorick_R

Why are these drugs becoming more popular than illicit street drugs?  The Post article cites two reasons.  First, these drugs are available.  Users will often shop around for doctors who will provide them with extra pills with minimal hassle.  Second, there is a common misconception that these pharmaceuticals are less dangerous or addictive than street drugs.  But the reality is, they activate the same opioid receptors as heroin.

Simply cracking down the amount of drugs handed out isn’t the solution.  Neither is making sweeping modifications that make it harder to get these drugs.  Many chronic pain patients rely on these medications to function, and their quality of life might suffer because of the irresponsible use of others.

It seems patient education is currently the best system for preventing addiction and abuse of prescription painkillers.  Every time another prescription for these medications is torn from a doctor’s tablet, a serious conversation about the proper use and risks of abuse should follow.

The idea of using biomarkers to predict diabetes is not entirely new. Glycated hemoglobin (HbA1C) values are now routinely being monitored to screen for at-risk patients. However, a new study in PLoS One shows that several new biomarkers in the blood may further our understanding of exactly who’s at risk for diabetes, and increase our knowledge of the etiology of the disease.

Veikko Salomaa and colleagues from the Department of Chronic Disease Prevention at the National Institute for Health and Welfare in Helsinki, Finland, tested nearly 13,000 people and found almost 600 cases of diabetes during routine follow-up exams.

According to the study, low levels of adiponectin, and high levels of apoB, C-reactive protein (CRP), and insulin, increase the chance that a woman will develop diabetes. When these factors were measured, proper diabetes prediction increased by 14% compared to when doctors only use classic risk factors, such as BMI and blood glucose levels, to predict disease.

The biomarkers that best predicted diabetes in men were low adiponectin, and high levels of CRP, interleukin-1 receptor antagonist, and ferritin. Accounting for these biomarkers led to a 25% increase in correct diabetes detection in the cohort.

read the study here.

One problem remains: you still have to get women into the clinic to be tested. However, a new study in the British Medical Journal shows that home testing is not only a reality, but it may actually boost compliance rates. Roughly 28% of women using the home testing kit, which consisted of a simple cervicovaginal lavage, effectively screened themselves, while only about 17% of women required to go into the doctor’s office for screening showed up.

The HPV DNA test is primarily looking for the high-risk virus serotype, and the authors of this study claim that home screening kits have the same sensitivity as the doctor’s protocol when specifically looking for the aggressive virus.

Special thanks to Lindsay Crouse for bringing this to my attention. In her email to me, she brilliantly summed up the significance of home HPV testing:

While screening has been tremendously successful in Western countries at reducing cervical cancer cases and deaths, the obstacle of reaching all women through screening remains. Currently, if a woman is to be screened for cervical cancer, she must visit a health care provider for a gynecological exam. If she is unable or reluctant to do that, whether due to transportation, cost, or comfort issues, she is less likely to get screened at all, and is consequently at increased risk for developing cervical cancer. More than half of such cancers are typically diagnosed in women who do not get screened regularly.

Researchers found that when “teenage” rats (30-45 days old) consumed massive amounts of sugar, they became extremely difficult to train as adults. For two weeks or so during adolescence, one group of rats had free access to a tasty 5% sucrose solution, while the control group only had water available. Similar to some American teenagers, the experimental group of rats consumed about 20% of their daily caloric intake as simple sugar.

To give you some background, it’s extremely easy to train adult rats to perform simple tasks, such as pulling levers or pressing buttons in return for a food reward. However, the researchers couldn’t motivate the rats that had consumed large amounts of sugar as teenagers to learn the task. My first reaction while reading this paper was: “Big deal. That group of rats just had sugar overload. It no longer had any real value for them, so there was no incentive to learn the new task”.

But here’s where the story gets interesting: if you repeat the experiment, but replace the teenage rats with adult rats, you get strikingly different results. When adult rats have free access to a sugary drink for two weeks, they never lose motivation for the sweet reward, and easily learn the new lever-pull task later in life. So it’s not that rats are simply sick of the sweet reward, but rather, it seems the sweet drink over-stimulated the reward pathway in the brain during adolescent development, leading to problems with motivation in adulthood.

Were the calories in the sugary drink or the sweet taste to blame for hyper-activating the reward circuits in the brain? To answer this, the authors took another group of teenage rats and gave them free access to a drink flavored with artificial sweetener, which has no calories. These rats were also unmotivated and rather difficult to train later in life, so the authors concluded that the sweet taste, but not the sugar itself, was hyper-activating the brain’s reward circuits.

Besides, ahem, crazy neuroscientists writing for health blogs, who cares about lazy rats? Well, the authors argue that a sign of depression in rodents is lack of motivation to perform simple tasks. Given that incidence rates for depression and other psychological illness are increasing in today’s society, it’s interesting to see how seemingly benign events during adolescence — a critical time in brain development — affect the mental state of adult animals.

However, I think we’re missing the bigger picture by focusing solely on LDL. First, it’s made us reliant on medication to solve a problem that can many times be addressed with changes in diet and exercise regimes. Once someone starts Lipitor treatment, they’ll be taking it for life, and if LDL levels don’t quite get as low as they should, it’s all too easy to solve the problem by increasing the dose. When patients first begin Lipitor treatment, physicians typically prescribe the lowest possible amount, 10mg. However, dosing can go as high as 80mg, which begs the question: Do higher doses of the drug really improve outcomes?

Second, the LDL value doesn’t tell the whole story. After all, some people that have low LDL levels, still develop heart disease. When your doctor orders a standard lipid panel, LDLs are measured along with other lipids, such as high-density lipoprotein (HDL) cholesterol and triglycerides. What role do these other types of lipids play in cardiovascular health?

Let’s start with the first question: Do higher doses of the drug really improve outcomes? This idea popped into my mind while reading a recent study in PLoS One that looked at LDL levels in patients diagnosed with familial hypercholesterolemia, a genetic predisposition to high levels of “bad” cholesterol. Caused by specific DNA mutations on a small region of chromosome 19, familial hypercholesterolemia drastically increases the chances that a person will develop heart disease. In fact, studies estimate that 85% of men with this mutation will have a heart attack by the age of 60.

The PLoS study found that only a minority of people with hypercholesterolemia brought their LDL levels down to recommended values, even when using statins. According to the authors, doctors were being too cautious with Lipitor dosing, and felt that higher doses would help patients reach their LDL targets.

Blood….beginning….to….boil…..

I know this isn’t the first time I’ve climbed up on my soapbox saying “more medication is not always the answer”, but I wanted to find proof. Lo and behold, I came across a good study from the New England Journal of Medicine that calculated the risk of a major cardiovascular event depending on whether people were taking low- or high-doses of Lipitor (10 or 80 mg, respectively).

Take a look at Figure 1: Higher doses of Lipitor only made a big difference in risk when HDL levels were low. As HDL levels rose, the difference in height between the light- and dark-green bars went down. This means that if a person can get his or her “good” cholesterol high enough, higher doses of Lipitor will NOT necessarily decrease the risk of having a cardiovascular event.

This finding ties in well with the second question: What role do these other numbers play in cardiovascular health? From the NEJM study, we’ve seen that high HDL levels – which are a good thing – trump higher doses of Lipitor in preventing heart disease. But can adequate levels of “good” cholesterol also counterbalance the cardiovascular risk when “bad”cholesterol levels are high?

In a word, yes. Take a look at Figure 2: as HDL level increased, the risk of a cardiovascular

event decreased. But more surprising, if HDL and LDL levels were both high (above 55 and 100 mg/dL, respectively), a person had nearly equal risk of a major cardiovascular event as someone who had good LDLs (<70 mg/dL) but bad HDLs (<38 mg/dL)!

Similar evidence is mounting that high triglyceride levels are also an independent risk factor for heart disease. In fact, one study showed that even when people with a history of heart problems used statins to lower their LDLs to acceptable levels, slight increases in triglyceride levels significantly increased the chance they’d have another cardiovascular event.

So there is evidence that the other lipids in the blood (HDL and triglycerides) are equally important in predicting heart health. So is it possible to raise your HDL, or lower your triglyceride, levels? You bet. Studies have shown that simple, endurance exercise training significantly decreases triglyceride levels and raises HDL levels in many people.

I’m not saying that diet and exercise changes will work for everyone. But statins shouldn’t be viewed as the magic bullet, either. As more studies on the science of exercise emerge, we’ll begin to move past the notion that exercise simply burns calories, and deepen our understanding of the complex interactions of physical activity and metabolism.

Having too much excess sugar in the bloodstream is never a good thing, and can lead to medical complications such as kidney failure, cardiovascular disease, and eye problems. But could high blood sugar also cause cancer? A Swedish research team addressed this question by tracking over 500,000 patients for 10-25 yeas, and published their results in the December issue of PLoS Medicine.

Similar to the findings of a study conducted in Korea in 2005, the European research team discovered that having elevated blood glucose levels increased the risk of developing certain types cancer later in life, such as pancreatic tumors in women and liver tumors in men. Not only had more cases of cancer occurred when people had high blood sugar, but the chance of survival also plummeted, especially when the person had cervical, espohageal, or colorectal cancers.

The authors present two theories on why elevated blood glucose levels could cause cancer: 1.) high sugar diets may cause an overproduction of insulin or insulin-like growth factor 1 (IGF-1), both of which promote rapid growth of new cells, the catalyzing step to tumor formation. 2.) More glucose in the blood stream could simply be adding fuel to the fire, feeding rogue tumor cells that need lots of energy to run.

Because the studies lacked certain controls, we can’t say for sure whether elevated blood glucose levels cause certain tumors to form. For example, the people with high blood glucose levels may have been sedentary, and so the lack of exercise may be what’s actually increasing the chances of developing cancer. Regardless, this study gives yet another example of unhealthy lifestyles contributing to comorbidity, a topic discussed at length by Thomas in The Decision Tree book.

As the hours crept by, the pain in my elbow worsened, until I woke up in the middle of the night with extreme arm pain. I immediately checked the elbow that had been swollen the previous day. The swelling had doubled in size, and the skin was an angry-red color. The following morning, I was back in the clinic, and my doctor started to suspect that this was no ordinary infection on my elbow, and may in fact be a drug-resistant staph infection. Gulp. Nonetheless, he felt confident that the clindamycin should clear it up.

Under the doctor’s orders, I spent the next day meticulously tracing the swollen area on my elbow with a Sharpie marker, carefully noting how much it spread. By the end of the day, my entire forearm was puffy and discolored, and my doctor said it was time for me to be admitted to the hospital. I spent 3 days there, getting intravenous treatments of vein-burning, gastrointestinal-rearranging Vancomycin pumped into my system. Not fun.

Afterward, I talked to a number of physician friends about my experience. They said my doctor’s treatment plan was textbook. He had done everything right. When docs suspect drug-resistant staph, the first line of defense is typically a hearty dose of clindamycin. The problem in my case was that the staph I contracted was actually resistant to clindamycin. That explains why the infection continued to spread even though I was taking the antibiotics.

Since this little microbial foray, I’ve had a growing interest in infectious disease. Specifically, I like seeing smart, new ways to keep tabs on how bacterium move from place to place. I wonder, if my doctor had known that clindamycin-resistant staph was infiltrating San Francisco, would I have initially received a different antibiotic? In my opinion, this was a clear case where having more data would have aided the diagnosis, and hastened a healthy outcome.

As Thomas pointed out at The Huffington Post, the true promise of personalized medicine is more about data than specialty drugs. Data can be our personal metrics, such as blood pressure, glucose levels, or cholesterol values. But keeping medical data to ourselves would be somewhat shortsighted. The internet has taught us the power of sharing data. We share our photos on Flickr. We share our status messages on Facebook. We share links on Twitter. Likewise, we can share our health and medical data, enabling pooled statistics from large populations. In the case of infectious disease, the best preventive strategy is to know exactly what strains you’re up against, and how the microbes are moving into different geographic regions over time.

Researchers recently confirmed the power of sharing microbial data in a new report, published this month in PLoS Medicine. Roughly 25% of us walk around with staph on our skin, yet not all of us get sick. That’s because there’s relatively few strains that cause serious symptoms. These so-called virulent strains are the ones docs want to track.

Following both methycilin-resistant (MRSA) and methycilin-susceptible (MSSA) staph strains through Europe, the authors coordinated the participation of 450 hospitals in 26 European countries, a logistic feat in its own right. When a case of staphylococcus aureus was found, the bacterium was genotyped (i.e. its DNA was analyzed to identify which strain it came from), and its location recorded. After collecting all the data, researchers could see how a particular strain of staph localized in different geographic regions. For instance, did the virulent strains stay in one hospital, or had they spread throughout the community?

The authors found that most virulent MRSA strains were contained in a health care clinic, meaning that drug-resistant staph was simply hopping from person-to-person within the hospital walls. Occasionally, that MRSA strain would show up at a different, nearby hospital, and rapidly spread in admitted patients. This implies that the carriers of the virulent MRSA strains are patients who are repeatedly admitted to different regional hospitals.

I’ll leave you with a final thought: tracking microbes isn’t just a task for researchers. In fact, DIYBio types should check out a cool new project called BioWeatherMap, which asks volunteers to swab commonly used public surfaces, such as door knobs or crosswalk buttons, to track pending microbial storm fronts.

Based on new research, Dobbs introduces the idea of two types of people, “dandelions” and “orchids”.  Dandelions can thrive anywhere, despite their environment or upbringing.  Orchids, however, are more temperamental, and require a stable environment to survive.  At first glance, the orchids may seem like a liability, and in fact, they often carry genes that make them susceptible to mood disorders and psychological disease.  The astounding part of Dobbs’ report is that he shows that given the right care, or environment, the orchids don’t just do OK, but far surpass the dandelions in perfomance.  In other words, given the right training, orchids may in fact be destined for greatness.

This finding redefines conditions we typically may have classified as undesirable.  ADHD, depression, and generalized anxiety disorder, are no longer conditions to dread, because given the right training, people with these predispositions may in fact be the true “movers and shakers” in the world.

Please read the full article for yourself.  And, as always, I’d welcome a discussion here…