Dental erosion is a different problem from dental caries. Dental erosion is defined as the removal of minerals from the tooth structure via chemicals. Dental caries are the result of increased site-specific acidity due to bacterial fermentation of sugars.
Still, both have the same general result, destruction of teeth structure.
Losing teeth probably significantly accelerated death among our Paleolithic ancestors, as it does with modern hunter-gatherers. It is painful and difficult to eat nutritious foods when one has teeth problems, and chronic lack of proper nutrition is the beginning of the end.
The table below, from Ehlen et al. (2008), shows the amount of erosion that occurred when teeth were exposed to beverages for 25 h in vitro. Erosion depth is measured in microns. The third row shows the chance probabilities (i.e., P values) associated with the differences in erosion of enamel and root. These are not particularly enlightening; enamel and root are both significantly eroded.
These results reflect a broader trend. Nearly all industrial beverages cause erosion, even the “healthy” fruit juices. This is due in part, but not entirely, to the acidity of the beverages. Other chemicals contribute to erosion as well. For example, Coke has a lower pH than Gatorade, but the latter causes more erosion of both enamel and root. Still, both pHs are lower than 4.0. The pH of pure water is a neutral 7.0.
Coke is how my name is pronounced, by the way.
This was a study in vitro. Is there evidence of tooth erosion by industrial beverages in people who drink them? Yes, there is quite a lot of evidence, and this evidence dates back many years. You would not guess it by looking at beverage commercials. See, for example, this article.
What about eating the fruits that are used to make the erosion-causing fruit juices? Doesn’t that cause erosion as well? Apparently not, because chewing leads to the release of a powerful protective substance, which is also sometimes exchanged by pairs of people who find each other attractive.
Reference
Leslie A. Ehlen, Teresa A. Marshall, Fang Qian, James S. Wefel, and John J. Warren (2008). Acidic beverages increase the risk of in vitro tooth erosion. Nutrition Research, 28(5), 299–303.
Monday, March 28, 2011
Monday, March 21, 2011
Health markers varying inexplicably? Do some detective work with HCE
John was overweight, out of shape, and experiencing fatigue. What did he do? He removed foods rich in refined carbohydrates and sugars from his diet. He also ditched industrial seed oils and started exercising. He used HealthCorrelator for Excel (HCE) to keep track of several health-related numbers over time (see figure below).
Over the period of time covered in the dataset, health markers steadily improved. For example, John’s HDL cholesterol went from a little under 40 mg/dl to just under 70; see chart below, one of many generated by HCE.
However, John’s blood pressure varied strangely during that time, as you can see on the chart below showing the variation of systolic blood pressure (SBP) against time. What could have been the reason for that? Salt intake is an unlikely culprit, as we’ve seen before.
As it turns out, John knew that heart rate could influence blood pressure somewhat, and he also knew that his doctor’s office measured his heart rate regularly. So he got the data from his doctor's office. When he entered heart rate as a column into HCE, the reason for his blood pressure swings became clear, as you can see on the figure below.
On the left part of the figure above are the correlations between SBP and each of the other health-related variables John measured, which HCE lists in order of strength. Heart rate shows up at the top, with a high 0.946 correlation with SBP. On the right part of the figure is the chart of SBP against heart rate.
As you can see, John's heart rate, measured at the doctor's office, varied from 61 to 90 bpm. Given that, John decided to measure his resting heart rate. John’s resting heart rate, measured after waking up using a simple wrist watch, was 61 bpm.
Mystery solved! John’s blood pressure fluctuations were benign, and caused by fluctuations in heart rate.
If John's SBP had been greater than 140, which did not happen, this could be seen as an unusual example of irregular white coat hypertension.
If you are interested, this YouTube video clip discusses in more detail the case above, from HCE’s use perspective. It shows how the heart rate column was added to the dataset in HCE, how the software generated correlations and graphs, and how they were interpreted.
Reference
Kock, N. (2010). HealthCorrelator for Excel 1.0 User Manual. Laredo, Texas: ScriptWarp Systems.
Over the period of time covered in the dataset, health markers steadily improved. For example, John’s HDL cholesterol went from a little under 40 mg/dl to just under 70; see chart below, one of many generated by HCE.
However, John’s blood pressure varied strangely during that time, as you can see on the chart below showing the variation of systolic blood pressure (SBP) against time. What could have been the reason for that? Salt intake is an unlikely culprit, as we’ve seen before.
As it turns out, John knew that heart rate could influence blood pressure somewhat, and he also knew that his doctor’s office measured his heart rate regularly. So he got the data from his doctor's office. When he entered heart rate as a column into HCE, the reason for his blood pressure swings became clear, as you can see on the figure below.
On the left part of the figure above are the correlations between SBP and each of the other health-related variables John measured, which HCE lists in order of strength. Heart rate shows up at the top, with a high 0.946 correlation with SBP. On the right part of the figure is the chart of SBP against heart rate.
As you can see, John's heart rate, measured at the doctor's office, varied from 61 to 90 bpm. Given that, John decided to measure his resting heart rate. John’s resting heart rate, measured after waking up using a simple wrist watch, was 61 bpm.
Mystery solved! John’s blood pressure fluctuations were benign, and caused by fluctuations in heart rate.
If John's SBP had been greater than 140, which did not happen, this could be seen as an unusual example of irregular white coat hypertension.
If you are interested, this YouTube video clip discusses in more detail the case above, from HCE’s use perspective. It shows how the heart rate column was added to the dataset in HCE, how the software generated correlations and graphs, and how they were interpreted.
Reference
Kock, N. (2010). HealthCorrelator for Excel 1.0 User Manual. Laredo, Texas: ScriptWarp Systems.
Monday, March 14, 2011
We share an ancestor who probably lived no more than 640 years ago
This post has been revised and re-published. The original comments are preserved below. Typically this is done with posts that attract many visits at the time they are published, and whose topics become particularly relevant or need to be re-addressed at a later date.
Monday, March 7, 2011
The China Study II: Fruit consumption and mortality
I ran several analyses on the effects of fruit consumption on mortality on the China Study II dataset using WarpPLS. For other China Study analyses, many using WarpPLS as well as HCE, click here.
The results are pretty clear – fruit consumption has no significant effect on mortality.
The bar charts figure below shows what seems to be a slight downward trend in mortality, in the 35-69 and 70-79 age ranges, apparently due to fruit consumption.
As it turns out, that slight trend may be due to something else: in the China Study II dataset, fruit consumption is positively associated with both animal protein and fat consumption. And, as we have seen from previous analyses (e.g., this one), the latter two seem to be protective.
So, if you like to eat fruit, maybe you should also make sure that you eat animal protein and fat as well.
The results are pretty clear – fruit consumption has no significant effect on mortality.
The bar charts figure below shows what seems to be a slight downward trend in mortality, in the 35-69 and 70-79 age ranges, apparently due to fruit consumption.
As it turns out, that slight trend may be due to something else: in the China Study II dataset, fruit consumption is positively associated with both animal protein and fat consumption. And, as we have seen from previous analyses (e.g., this one), the latter two seem to be protective.
So, if you like to eat fruit, maybe you should also make sure that you eat animal protein and fat as well.