Iron is essential for life – every cell in your body needs iron to function. Understandably, athletes are often concerned about iron, because iron is part of hemoglobin in blood and myoglobin in muscles, helping deliver oxygen to cells. Low hemoglobin can result in fatigue and decreased aerobic capacity, leading some athletes to assume that extra iron will enhance performance. Indeed, some endurance athletes take iron supplements regardless of their iron status, even though excess iron might compromise their health. On the other hand, truly iron deficient athletes might not be aware of their status, and changes in their diet or iron supplements might reduce fatigue or improve performance. But dietary changes aren’t straightforward because iron absorption is a complicated phenomenon. Read on to find out
Endurance athletes, particularly females and adolescents, are at risk for reduced iron stores and anemia (reduced blood cell mass or hemoglobin concentration). Intense training increases iron demands, and exercise can deplete iron stores through increased red blood cell production, tissue inflammation, sweating, and destruction of red blood cells with impact (foot strike). Adolescents are growing rapidly and iron demands are high, and women lose iron through menstruation. Studies show that adolescents and women don’t consume enough iron in their diet, and experts believe that this inadequate intake is a major contributor to iron deficiency.
Athletes should consult a physician to get a blood test to screen for iron deficiency. Typically your doctor will look at complete blood count (CBC) measures, serum ferritin (estimate of stored iron) and possibly other specific tests to help diagnose low iron. A diagnosis of anemia often involves (1) low hemoglobin; (2) smaller than normal red blood cells; and (3) low serum ferritin.
A low ferritin level alone could be an early warning sign of anemia, so athletes with low ferritin should try to increase their iron status first by increasing the iron in their diet.
The for daily iron intake are as follows:
Who Needs More? Vegetarians and vegans should try to consume more iron (up to 1.8 times more), as these recommended intakes are based on the assumption that at least 10% of iron intake is from heme iron; female athletes engaging in weight bearing activities (i.e., runners) should also try to consume more iron to account for iron losses due to foot strike; pregnancy increases demands to 27 mg.
To increase your iron stores, you should consume a healthy diet that includes a wide variety of iron-containing foods. Foods contain two types of iron: heme iron is found in red meats, fish, and poultry, and non-heme iron is mostly from plant sources (enriched and whole grains, beans, nuts and some vegetables and fruit) as well as eggs and dairy products. About 60% of the iron in meat is non-heme. Here are common dietary sources of iron.
Figuring out the iron content of foods based on food labels is tricky. Although iron requirements vary by age and gender (and pregnancy, athletic, and vegetarian status), the Nutrition Facts Table for foods only has one value for iron. You need to look at the % daily value panel, and know what value it is based on:
How well you absorb iron might be as important as the amount that you consume. But iron absorption is a complex phenomenon: your body only absorbs about 10-15% of the iron you eat, and the amount of iron you absorb from a food is influenced by (1) your body iron status; (2) the type of iron (heme iron is better absorbed than non-heme iron); (3) iron inhibitors; and (4) iron enhancers.
Iron Inhibitors are substances in foods that interfere with iron absorption (especially non-heme sources of iron). These include
You’ll notice that this list includes many healthful foods, and the health consequences of limiting or avoiding these foods (not to mention meal-planning headaches) likely outweigh the possible iron boosting benefits of avoiding them. My advice? Focus on Iron Enhancers.
A main focus should be to include vitamin-C rich foods with your meals and snacks. Here are some other tips and meal ideas:
The ability for organizations to data mine and address a wide range of business problems across many industries is a huge asset in terms of ROI. What about big data and analytics helping not only the bottom line but also the general public? What about all the data that envelops a single patient and his/her likeliness to do a set of ordinal outcomes?
Let’s consider patients with type one diabetes. Constantly these patients are asked to regurgitate a string of numbers that make up their medical history and how their blood glucose levels have been performing. Luckily, due to drastic improvements in technology, a diabetic’s meter can hold up to three months of glucose readings, insulin, and carbohydrate intake. But what is actually becoming of this data? Most doctors focus heavily on line-by-line reports, circling where they see hyper or hypo-glycemia, but there are many more interactions that play into the patients’ blood glucose levels in the next month.
Combining my interest in analytics and my enthusiasm for volunteering with (Juvenile Diabetes Research Foundation), I was able to randomly generate information that mimicked what diabetics can download off of their devices in the form of a CSV file. Not only were their blood sugar readings in there, but there was insulin intake, carb intake, types of insulin delivered, whether it was before a meal or after a meal, and when they exercised, all in perfect comma-separated columns with consistent time stamps. With little to no experience in R but an intense passion for data analysis, I taught myself how to script inside the R console.
I developed a script that pulls in a patient’s CSV automatically from their device, cleans the data as needed, and appends the incoming results to a master file, which generates reports including insightful visualizations for quick interpretations. The most appealing part of the process is the creation of time series projections for blood glucose readings that account for interactions from carbohydrate intake even when doctors make a change to their patients’ insulin regimen. But it wasn’t enough. After watching tutorials in , bothering Dr. LaBarr, and talking with my classmates, I developed a that a patient can now send to their doctor and view updates.
I know this is still not enough; a diabetic is producing data even as I am writing this column, and I am excited about implementing new techniques I am learning at the Institute. What about automating it? What if I can develop something that does this automatically every two weeks without running a program? What if insulin pumps can see these results and adjust insulin levels automatically? Can this eventually be done? I guess it depends. One thing I know for sure is that enthusiasm for big data can impact businesses and the world.
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