At the University of Georgia, a group of nutrition professors are investigating how obesity may affect folate metabolism during pregnancy. With an increasingly obese population and folate recommendations based on studies from the 1990s, they surmise that obese mothers may need more folate during pregnancy.

They recently conducted a pilot study of both normal weight and obese women to measure how the recommended dose — 400 micrograms — is metabolized in the blood over 10 hours. The study, published in the International Journal of Obesity earlier this month, shows that there’s a significant difference between the two groups.

Serum folate response follow a single dose of folate (Silva et al, Int J Obesity, 2013)

The researchers administered a dose of folic acid to 16 normal-weight women with a BMI between 18.5 and 24.9 and 16 obese women with a BMI over 30.  Before the folate was administered, fasting serum folate was lower among participants in the obese group, while their red blood cell folate was higher. During the first three hours after folate administration, serum folate levels were 34% lower in the obese women, and the overall folate response after 10 hours was also lower in obese women. 

Area under the curve for serum folate following folate ingestion (Silva et al, 2013, Int J Obesity)

“It’s quite important to determine whether or not this recommended amount of folic acid should be tailored and based on body weight,” said Lynn Bailey, the lead researcher. “In the pharmaceutical arena, many drugs are prescribed based on body weight, but that’s not the case for this vitamin.”

Getting enough folic acid before and during the first month of pregnancy prevents most neural tube defects — birth defects of the brain and spinal cord — that lead to spina bifida, nerve damage, paralysis, or an undeveloped brain. Folate can reduce these rare neural tube defects from about 2 in 1,000 births to about 1 in 1,000, according to the Office of Dietary Supplements.

Bailey, who has studied folic acid, maternal health, and birth defects for more than three decades, has previously published research that shows obesity is associated with an increased risk of having a pregnancy affected by a neural tube defect.

“We really think this research has translational potential because it could result in a change in the way the public health recommendations are defined,” she said. “If it is based on BMI, we could overcome the negative impact of obesity on developing embryos that don’t receive a sufficient amount of folic acid.”

The U.S. Preventive Services Task Force and Office of Dietary Supplements have recommended 400 to 800 micrograms of folic acid for women of child-bearing age since 1996, and USPSTF reaffirmed the recommendation in 2009. Spokespeople from both groups said further research is needed to determine whether a change is warranted. USPSTF reviews recommendations every five years, and folic acid isn’t currently on the list for USPSTF to update.

“To update this recommendation, the task force will examine all available and current evidence on this topic,” said Mark Ebell, a University of Georgia professor of clinical epidemiology and member of the USPSTF. “We must determine the balance of benefits and harms for folic acid supplementation.”

A new Centers for Disease Control and Prevention study, published in the Journal of Nutrition this month, states that though the majority of women do use folate during pregnancy, only half of pregnant women are using the supplement during the first trimester, when it matters most.

“We found the good news that a majority of pregnant women did report taking a supplement, but it was important to look at the breakdown by trimester,” said Amy Branum, author of the paper and staff member at the CDC National Center for Health Statistics. “We wanted to see how many in the first trimester were taking folic acid because it’s a critical period for neural tube defect development.”

Branum and co-workers investigated data about nearly 1,300 pregnant women surveyed from 1999 to 2006 in the National Health and Nutrition Examination Survey, an annual CDC survey used to evaluate health in the nation. Previous analytical studies haven’t tracked folate use and status in particular, especially by marking trimesters through red blood cell data.

More than three-fourths, or about 77 percent, said they used a supplement in the previous 30 days, usually a multivitamin with folic acid and iron. About 55 percent of women in their first trimester took a supplement compared with 76 percent in their second trimester and 89 percent in their third trimester. Red blood count folate status, which indicates the actual presence of folate in the blood, was also lowest in the first trimester and highest in the third trimester.

“It was nice to study recent data of pregnant women,” said Branum, who has researched longitudinal studies about pregnant women in the 1950s and 1960s. “We’re finding ways to use this data and make it useful to the public and medical community.”

Carolyn Crist

Carolyn Crist is a freelance writer pursuing her master’s degree in Health and Medical Journalism at the University of Georgia. She graduated from UGA in 2010 with degrees in newspapers and English and worked as an education and political reporter.

The first survey, available here, is aimed at the general public, and is trying to determine their (e.g. your) opinions about body weight, health, and related policies.

The second study, available here, is aimed at people who work in the treatment of obesity (physicians, nurses, dietitians, psychologists, etc) in order to inform the development of a “Healthy Body Scorecard” for children.

I found out about both of the above studies via the Canadian Obesity Network, which makes this a good time to remind everyone that their 2nd National Obesity Summit will be taking place May 1-4 in Vancouver.  Peter and I liveblogged the previous Summit in Montreal (those posts can be found here), and it was a fantastic experience.  Early bird registration lasts until this Friday (April 19), so if you’re thinking about going now is the time to sign up.  You can sign up for the conference here.

Travis

Obesity is a complex and multifactorial chronic disease that remains a military and public health priority in the United States. Recently, we’ve identified a strong association between obesity prevalence and altitude within the US. Our findings were surprising because they indicated the magnitude of this association was large and the pattern of association exhibited a curvilinear dose response in 500 meter categories of altitude. There was a 4-5 fold increase in obesity prevalence at low altitude as compared with the highest altitude category after controlling for diet, activity level, smoking, demographics, temperature, and urbanization. We published our findings in the International Journal of Obesity (advance online publication doi:10.1038/ijo.2013.5) and presented at the 2013 American College of Preventive Medicine conference.

The process we used is easily reproducible. We combined several publicly available national datasets using statistical software and geographic information systems using the county of residence as a common linkage across datasets. For a basic visualization, Figure 1 shows the Centers for Disease Control and Prevention’s publicly available map with projected obesity prevalence for each county adjusted only for age. This map was created based on similar data as the source for our study, but we used actual self-reported height and weight rather than the modeling shown in Figure 1 and we adjusted for age, sex, race/ethnicity, physical activity compliance, fruit and vegetable consumption, smoking status, employment status, education, urbanization, temperature category and income. By comparison, Figure 2 from http://ned.usgs.gov/ shows the topography of the United States.

Figure 1. Age Adjusted Obesity Prevalence by County. This image was obtained from cdc.gov/diabetes, but this particular map represents obesity prevalence and not diabetes.

Figure 2. A topographical map of the USA (source). Note the similarities with Figure 1.

While it is always important to remember correlation does not prove causation, in this case, we already know hypoxia causes anorexia and weight loss based on well controlled interventional data.  This effect is biologically plausible based on the relationship between hypoxia and leptin signaling, norepinephrine and sympathetic tone, non-erythroid erythropoietin receptor signaling, and the metabolic demands at high altitude.  We hope additional research will help clarify the mechanisms and long term health effect of either high altitude residence or normobaric hypoxia.  These results, showing a large magnitude of association, provide some optimism that this is a worthy line of research.

Jameson Voss

About the Author: Jameson Voss is a third year Preventive Medicine Resident at the Uniformed Services University of the Health Sciences.  His research focus is on obesity. 

Voss, J., Masuoka, P., Webber, B., Scher, A., & Atkinson, R. (2013). Association of elevation, urbanization and ambient temperature with obesity prevalence in the United States International Journal of Obesity DOI: 10.1038/ijo.2013.5

Actical accelerometer users rejoice! Rachel Colley of the Healthy Active Living and Obesity Research Group at the Children’s Hospital of Eastern Ontario Research Institute has posted annotated SAS files to use for cleaning and combining accelerometry data from multiple participants.

The Accel+ program is free to download:

• User’s Guide (Download)
• SAS Code
  • Step 1: Read and Stack (Download)
  • Step 2: Quality Control (Download)
  • Step 3: Derive Variables (Download)
  • Step 4: Advanced Analysis (Download)

Contrary to the title of this post, the files aren’t foolproof (the HALO site includes a disclaimer to that effect), but they are extremely helpful for folks like myself who know a little bit about SAS, but aren’t experts.  These Accel+ files can also be viewed as essentially companion files to the National Cancer Institute’s publicly available files for cleaning Actigraph accelerometer data.  I really can’t say enough about how great it is that Rachel and her colleagues (Didier Garriguet, Glenn Glover, Elyse Labonte and Janine Clarke)made these files available for anyone who wants to use them.

The casual reader may wonder why I could possibly be so excited about this.

Well, when researchers want to measure physical activity and/or sedentary behaviour, they usually choose one of two approaches.  They can ask people how much time they spend being active and/or sedentary (e.g. self-report), or they can measure it directly using an accelerometer (there are other approaches you can use, but these are by far the most common).

Anyone who has ever used accelerometry data will know that it can be tricky to analyse.

This has to do with 2 basic issues:

1.  Accelerometers provide an unbelievable amount of data

The two major accelerometers on the market (Actigraph and Actical) give a movement “count” for each minute of the day (1440 datapoints/day), and researchers usually collect data for at least 7 days.  If you  have a study with 1000 participants (which isn’t that large as far as epidemiological studies go), you wind up with a file that has 10080000 (1440*7*1000) data points.  That’s for a smallish epidemiological study.  To put this in perspective, you can only fit the data of 4-5 participants into an Excel spreadsheet before you get an error message telling you that you’ve run out of cells.

Let’s say that one of the accelerometers malfunctions – you do not want to sift through 1 million datapoints by hand in order to find it.  As a result of this giant tsunami of data, we tend to use code-based programs like SAS to clean and analyse accelerometer data.  And since these programs are code-based, they require that you literally learn a new language to use them.  So analysing accelerometer data can be tricky, especially for physiologists who don’t have a biostatistics/epidemiology background.

2.  It can be tricky to be consistent across studies

While accelerometer data is “objective”, there are a lot of somewhat arbitrary decisions that need to be made when cleaning the data (they are usually based on evidence, but still somewhat arbitrary).  For example, should you include participants who only wore the accelerometers for 6 hours/day, instead of the requested 14?  Should you include participants who only wore the accelerometer on weekends, but not weekdays?  How should you decide what values constitute various intensitites of physical activity (light, moderate, vigorous), and how do you determine whether a participant is being sedentary, or whether they took off the accelerometer altogether?

If I decide to include all the data, and you decide to only include data from participants who wore the accelerometer for 10+ hours/day over at least 4 separate days, then you and I are going to wind up with a very different values based on the same dataset.  For the true nerds out there, check out this recent paper from MSSE which illustrates just how these minor changes can affect the relationship between movement and health.

Accelerometers come with their own software for downloading and analyzing data, but it doesn’t really work for the purposes of research.  Hence why programs like Accel+ are so incredibly useful.  Playing with these sorts of pre-made files can also be a very good way to figure out how to use SAS, if you’re into that sort of thing.

If you are a researcher who will soon be undertaking an analysis of Actical accelerometry data, download the above files and get analyzing!

Travis

What’s even more interesting is that most seem to have forgotten that basically an identical study had already been published back in 2008 in one of the world’s top medical journals. In fact, we covered this study nearly 5 years ago on this very blog!

That not-so-new study suggested that monetary reward may be a better motivator for behavior change and ultimately, weight-loss, than the commonly touted health benefits.

Here’s the details of that peer-reviewed and published study:

Dr. Volpp and colleagues tracked the weight change in 57 obese individuals (30-70 years of age) who were randomized to either a no-treatment control group or to 1 of 2 financial incentive programs (a lottery incentive group, or a deposit incentive group). All participants were instructed to lower their weight (via diet and exercise) by 1 lb per week for the duration of the 16 week intervention, thus aiming for a total target weight-loss of 16 lbs. Individuals in the incentive groups received their financial rewards on a monthly basis, only if they had met or exceeded their target weight loss (1lb/wk). Those that failed to make the weight-loss goal were merely told how much money they would have received if they had been successful, whereas the control group received no reward regardless of their progress.

Over the course of the 4 month intervention individuals in the incentive groups earned an average of approximately $300, in contrast to $0 awarded to those in the control group. Interestingly, the average weight loss achieved by those receiving a financial incentive was significantly greater as compared to that of the control group (13-14lbs vs. 4 lbs, respectively). Furthermore, only 10% of individuals in the control group versus approximately 50% of those in the incentive groups achieved the target weight-loss of 16lbs.

However, during a subsequent 3-month follow-up, study participants gained back much of the lost weight after the cessation of the financial incentives – a finding which is common to most, if not all, weight-loss intervention studies.

This study extends findings of a previous investigation in which participants who were offered $14 per percent decrease in weight lost about 5lbs, while those who were offered no compensation lost 2lbs during the 3 month intervention.

So how can any of this be applied in the real-world?

The thinking goes – if an overweight individual has previously had trouble adhering to a diet and/or exercise program, investing some of his/her own money may provide a novel incentive to stay on track in order to avoid losing money – the basic concept of loss aversion.  For example, you can hand over $100 to a trusted friend/spouse/family member and sign a contract before embarking on a lifestyle change. This trusted individual is instructed to return the money in full if you achieve your goal, or otherwise to donate your money to a cause that you find distasteful – like the NRA or the Church of Scientology.

More than anything else, its a cute and gimmicky approach to providing incentive for weight loss, and the idea makes for great headlines (as recently illustrated). I’m sure financial incentives can work for some, but this is no obesity panacea.

Peter

Circadian rhythms are biological cycles that occur in humans, animals, insects, plants, and even bacteria with a period of approximately (circa) one day (diem). These rhythms are determined internally by a part of our hypothalamus and are synchronized perfectly to our 24-hr days by the sun and other cues. This internal clock mediates daily variation in everything from hormone levels, to sleep/wake cycles, feeding behaviour, thermoregulation, to bowel movements and cardiovascular function, among many others.

It is largely due to these predictable circadian rhythms that risk of a myocardial infarction (heart attack) is significantly highest in the morning (by about 40% as compared to other times in the day). Right as we awake, our cardiovascular system is in the most compromised state –systolic blood pressure and heart rate show the largest upward spike in the morning, blood vessels ability to dilate in response to increased blood flow is compromised (relative endothelial dysfunction), blood clots are more likely to form, and the ability to break them up is at its lowest point in the day. Is it any wonder then, that the first snowfall – shoveled early in the morning by people at risk – always leads to a spike in heart attacks?

Interestingly, the 1hr shift experienced by citizens of many countries (most notably Europe and North America) during the fall and spring in accordance with daylight savings time also has a detrimental effect on cardiovascular risk. The problem lies in the fact that our circadian clock takes time to adjust, and it is best adjusted by changes in day/night or light/dark cycles – not simply the adjustment of our watch. Thus, the few mornings after the clock change our internal clocks are at odds with our watches and particularly in the spring – when one hour of sleep is lost, we wake up with our cardiovascular system being in an even more compromised state than normal.

A 2009 study in the New England Journal of Medicine clearly shows this effect. In the study the authors investigated the number of heart attacks in Sweden the week before and the week after the 1hr clock changes in both the spring and fall. As would be predicted, individuals had an approximately 5% greater risk of having a heart attack immediately after the ‘spring ahead’ clock change compared to the previous week.

The authors rightfully suggest that individuals at risk of cardiovascular complications would be better off changing their clocks more gradually (i.e. by 15 minutes, starting on the Friday before the change). More importantly, avoiding strenuous activity and stress right in the morning may also be a valid suggestion.

An even better strategy from a public health standpoint would be to do away with daylight savings time altogether.

Peter