But things are back in full swing now, and I’ll have a number of new stories in the next few weeks, so stay tuned.

A few days ago, I wrote a piece for Slate’s DoubleX blog, on a PLoS Medicine study where researchers created a prediction model that they say will accurately determine if someone will get pregnant with in vitro fertilization (IVF).

Rather than externally validating their model, the researchers are crowdsourcing their new tool, and have opened it up on the web and a soon-to-be-released iPhone app. I wrote:

By answering nine questions about pregnancy history, the source of the eggs, and the types of fertility medications used, couples can find out their odds of successful IVF, as well as learn how each variable affects their risk profile. For instance, imagine a 33-year-old woman who’s never been pregnant and is using IVF for the first time after a year of trying to get pregnant on her own. Her fertility problems are caused by cervical issues, but she’s still using her own eggs (and has had gonadotropin hormone therapy treatment). According to IVFPredict, her chance of having a baby with her partner via in vitro fertilization is 13 percent. If the couple decides to go with intracytoplasmic sperm injection instead of the normal IVF method of combining multitudes of sperm and eggs in a dish, their odds jump to 42 percent, according to the model.

It’s great to see clinical decision/patient education tools emerge for couples trying to get pregnant, especially those turning to IVF — a procedure that costs about $12,000 for each procedure, and is often not covered by health insurance.

Image via Flickr / Yutaka Tsutano

Raymond McCauley and a small team of DIY-researchers wanted to know how effective different types of vitamins were for clearing “undesirable” amino acids from their bodies.   One of the supplements they investigated was a standard, over-the-counter vitamin B tablet.  But the other, was a more highly active B vitamin, called L-methylfolate.  McCauley’s genetic profile differed from the rest of his team in two particular SNPs (he was homozygous at both locations).

For four of the study participants, all of whom paid out of pocket to participate in the research, either type of vitamin supplement decreased homocysteine levels by almost a third, indicating that the vitamins were having the desired effect and leading to homocysteine getting converted into more benign amino acids. But for McCauley—the only person in the study who was homozygous at both SNPs tested—run-of-the-mill pills raised his homocysteine concentrations, and only the more active L-methylfolate seemed to aid his vitamin metabolism. After completing the experiment last month, McCauley changed his source of supplementary vitamin B to L-methylfolate.

Preliminary evidence has shown that in the normal population, the higher the C-reactive protein level, the higher the risk for CVD. But what exactly is a normal population? These days, a full serving of heart disease often comes with a heaping side of Type II diabetes, rheumatoid arthritis, or chronic kidney disease, creating a so-called “co-morbidity” of chronic diseases. Not surprisingly, these secondary disease states also affect the levels of C-reactive protein in the blood. So when a patient has more than one chronic condition, how useful is measuring the C-reactive protein level in predicting CVD risk?

A new study published this week in PLoS One by a group at Kings College, London, took a look at people with rheumatoid arthritis (RA), an inflammatory joint condition that also coincides with remarkably elevated C-reactive protein levels. According to the authors, along with swollen joints, sufferers of rheumatoid arthritis are also twice as likely to have a heart attack.

The researchers looked at three subclinical measures of CVD: flow mediated dilation (measures endothelial cell function), intima-medial thickness (measures arterial wall thickness), and pulse wave velocity (measures large artery stiffness), in people with RA and healthy control subjects. The RA group was further subdivided into three tiers according to how much C-reactive protein was circulating in the patient’s blood during a baseline reading.

If C-reactive protein was in fact causing CVD in rheumatoid arthritis patients, the subclinical CVD measures should incrementally change as the level of C-reactive protein increases. However, the researchers found that two of the subclinical measures didn’t change at all as the level of C-reactive protein increased in those with rheumatoid arthritis. The third subclinical measure – the flow mediated dilation value, which measures how responsive endothelial cells are – actually improved as C-reactive protein levels rose, suggesting that the protein may offer a protective function in a chronic state of inflammation.

I’m always scouring scientific papers looking for the next great thing in biomarkers. After all, if a simple blood test can tell us who’s at risk for certain diseases, we could make great strides in diagnosis and treatment of affected people. But I think this paper shows that biomarker readings are not so straightforward. In the complicated web of chronic disease we’re now spinning, we need to better understand how cardiovascular disease biomarkers – particularly inflammatory markers — change when people have more than one chronic medical condition.

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010242#pone-0010242-t001

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.

In her post, Susannah said that Esther Dyson helps her understand that even though the Health 2.0 crowd is relatively small right now, these e-patients provide a glimpse of how powerful and interactive health care can become in the future.

For similar reasons, expecting moms give me hope for the future of Health 2.0.  They constantly read up on the latest baby health information.  They post comments on blogs, forums, and social networks, sharing insider tips and trends.  

Last week, I attended the quasi-monthly San Francisco Quantified Self meetup.  We had yet another spectacular night of presentations by people who are tracking some aspect of their life, whether it’s health data, fitness trends, or a complex analysis of how they spend every minute of the day.  The meetup was hosted by MedHelp, a company that’s created a large medical social community.  I was particularly struck by their presentation on their suite of personal health trackers, which included a ‘Pregnancy Symptom Tracker’.

With MedHelp’s online application, women can easily track the symptoms they’re experiencing, such as morning sickness, fainting incidents, and mood swings.  But in true Web 2.0 fashion, the true power of the application is realized when users share their data with others.  By pooling the data of many users, MedHelp is defining, in near real-time, the norms of pregnancy. For example, their results show that morning sickness peaks around week 8, but falls off rather quickly later in the pregnancy.  Fainting spells are far more common right before birth than at any other time.  Alas, there’s no relief from mood swings.  They occur in 40% of women, and are fairly constant while pregnant.

I hope that in the future, data like this will arm people with the information to decide if a certain symptom is normal, or whether it’s time to see a doctor.  After all, making decisions based on data will not only help people save dollars at the doctor’s office, but will also lead to better health.

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.

What about prescription medication? Sure, I can easily make a list of the meds I’m currently taking, or get my doctor to hand this list over if memory fails me. But how much do I really know about these drugs? Most people, myself included, take our doctor’s word when he or she decides to put us on a commonly prescribed medications. For example, let’s say a patient has blood-work that shows elevated LDL cholesterol on two consecutive screenings, comes from a family where cardiovascular disease runs rampant, and was previously unable to regulate cholesterol levels with strict diet and exercise regimes. If the doctor prescribed Lipitor to treat the problem, a patient may not even think twice about taking it. After all, we see commercials for such drugs on our TV, and we flip past their ads in our magazines. Direct-to-consumer marketing by pharmaceutical companies makes drugs familiar and, presumably, safe.

But regardless of what advertisements say, the FDA is ultimately responsible for giving drugs the safety stamp of approval. The decision to approve a drug is based on substantial amounts of preclinical (testing in animals) and clinical (testing in humans) data submitted to the FDA by the drug manufacturer.

Let’s say someone – a doctor, a patient, a concerned citizen – wants to review the data that the FDA uses to approve a drug. If the drug you’re taking was approved after 1998, you can find the FDA’s review documents online. If you’re prescribed an older medicine, you may strike out when trying to find what the FDA has to say about it. The government’s information on drugs approved through 1997 may be released if someone makes a request through the Freedom of Information Act, but the FDA reserves the right to not publish reports if the agency deems the preserved documents are of “poor quality”. In fact, Watzman found that online safety information is missing for 9 of the 25 most commonly prescribed drugs.

For older and newer drugs alike, when the FDA publishes a review online, it’s never in a text-searchable format. Rather, the agency prints the original paperwork, edits with white-out to cover “propriety information”, scans the newly edited document, and finally, posts the altered PDFs online.

Are these edited documents, with words covered with white-out and entire sections omitted, really providing us with useful information? Somewhere along the drug approval process, there has to be a succinct memo that circulates around the FDA headquarters describing the agency’s major findings on a particular drug. Why can’t the FDA publish a simple summary of their findings?

Skeptics may argue that detailed information about a drug already published by pharmaceutical companies in medical journals, such as the New England Journal of Medicine (NEJM) or the Journal of the American Medical Association (JAMA), should suffice. However, there are two problems with the information reported in medical journals. First, most of these journals are not open access, meaning the average consumer cannot access them without paying for the article. Second, many drug companies only publish positive findings, and bury negative results that show less than desirable efficacy or safety.

Watzman’s report is an eye-opening look at what’s wrong with the flow of drug information as it goes from pharmaceutical company to the FDA to the consumer. If pharmaceutical companies are allowed to market directly to consumers, we should demand the right to know what the FDA has to say about the safety of these drugs.

Data from our bodies is no exception — physiologic data can now be accessed as a real-time data stream thanks to personal health monitors. But does the vast amount of data we get from our bodies make us any healthier? Do we need to collect data 24-hours a day in order to learn something interesting about our health? Is it even feasible to wear these sensors all day, every day?

I am embarking on a new self-tracking experiment to answer these questions (and possibly a few others). For 30 days, I will be using devices such as the Zeo personal sleep coach, the Philips DirectLife activity monitor, the Mio Motiva wristband on-demand heart rate monitor, and the Nike+ sportband. The goal of this study is not to pit one device against another; rather, I want to focus on what the data tells me, and how I can best use it to stay healthy.

I’ll get a blog post up here at least once a week, all the while working on a longer story about the journey that will be released at the end of the month.

Stay tuned. It should be a fun ride…

“The more food records people kept, the more weight they lost,” says Jack Hollis PhD, a researcher at KPCHR and lead author of the study published in the August issue of the American Journal of Preventive Medicine. “Those who kept daily food records lost twice as much weight as those who kept no records. It seems that the simple act of writing down what you eat encourages people to consume fewer calories.

“Every day I hear patients say they can’t lose weight. This study shows that most people can lose weight if they have the right tools and support,” says Keith Bachman, MD, a Kaiser Permanente internist and weight management specialist. “Keeping a food diary doesn’t have to be a formal thing. Just the act of scribbling down what you eat on a Post-It note, sending yourself e-mails tallying each meal, or sending yourself a text message will suffice. It’s the process of reflecting on what we eat that helps us become aware of our habits, and hopefully change our behavior.”

The study concluded what proponents of self-tracking have known all along, namely, that monitoring your own actions creates a heightened self-awareness. Sure, fancy new iPhone apps where you track your weight or blood-sugar over time are cool, but self-tracking doesn’t have to go hand-in-hand with technology.

Case in point, on my last visit to my mother’s place, I found a home blood pressure monitor and a piece of paper with scribbled numbers on it sitting on a table in the living room. She told me that she has been tracking her blood pressure every day for the past months, and writing the numbers in her notebook log. I had been tracking my running data for years using elaborate web programs, and complicated sensors. But suddenly I realized that self-tracking doesn’t have to be limited to the tech savvy or early adopters; a pen and a piece of paper will do the trick.

A few weeks ago, I posted a summary of a few gadgets that tracked personal metrics (steps taken, sleep quality, etc.).  I highlighted these particular devices because they took some of the hassle out of self-tracking by automating the process of collecting data and storing it in a personal database on the device’s website.

Following up on that post, I read an article at Technology Review today that talked about personal data monitors that interface directly with Microsoft’s electronic medical record system, Health Vault.  Now, when you step on a scale or take your blood pressure (with compatible devices), your personal metrics will not only streamline to a single site, but will also associate with your health record, which will make this information easier to share and discuss with your physician.

The article says that interfacing personal health metrics with electroinc medical records is a step in the right direction, but it stil requires the user to physically “do” something (i.e. step on a scale, or take a blood pressure measurement).  However, soon it may become easier for us to monitor our personal metrics, as a quote from the story says that in the future, “…Band-Aid-like sensors on the skin might monitor blood pressure or heart rate continuously”.