Lifeworld

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.

This post is based on an analysis of a subset of the China Study II data, using HealthCorrelator for Excel (HCE), which is publicly available for download and use on a free trial basis. You can access the original data on the HCE web site, under “Sample datasets”.

HCE was designed to be used with small and individual personal datasets, but it can also be used with larger datasets for multiple individuals.

This analysis focuses on two main variables from the China Study II data: mortality in the 35-69 age range, and mortality in the 70-79 range. The table below shows the coefficients of association calculated by HCE for those two variables. The original variable labels are shown.


One advantage of looking at mortality in these ranges is that they are more likely to reflect the impact of degenerative diseases. Infectious diseases likely killed a lot of children in China at the time the data was being collected. Heart disease, on the other hand, is likely to have killed more people in the 35-69 and 70-79 ranges.

It is also good to have data for both ranges, because factors that likely increased longevity were those that were associated with decreased mortality in both ranges. For example, a factor that was strongly associated with mortality in the 35-69 range, but not the 70-79 range, might simply be very deadly in the former range.

The mortalities in both ranges are strongly correlated with each other, which is to be expected. Next, at the very top for both ranges, is sex. Being female is by far the variable with the strongest, and negative, association with mortality.

While I would expect females to live longer, the strengths of the associations make me think that there is something else going on here. Possibly different dietary or behavioral patterns displayed by females. Maybe smoking cigarettes or alcohol abuse was a lot less prevalent among them.

Markedly different lifestyle patterns between males and females may be a major confounding variable in the China Study sample.

Some of the variables are redundant; meaning that they are highly correlated and seem to measure the same thing. This is clear when one looks at the other coefficients of association generated by HCE.

For example, plant food consumption is strongly and negatively correlated with animal food consumption; so strongly that you could use either one of these two variables to measure the other, after inverting the scale. The same is true for consumption of rice and white flour.

Plant food consumption is not strongly correlated with plant protein consumption; many plant foods have little protein in them. The ones that have high protein content are typically industrialized and seed-based. The type of food most strongly associated with plant protein consumption is white flour, by far. The correlation is .645.

The figure below is based on the table above. I opened a separate instance of Excel, and copied the coefficients generated by HCE into it. Then I built two bar charts with them. The variable labels were replaced with more suggestive names, and some redundant variables were removed. Only the top 7 variables are shown, ordered from left to right on the bar charts in order of strength of association. The ones above the horizontal axis possibly increase mortality in each age range, whereas the ones at the bottom possibly decrease it.


When you look at these results as a whole, a few things come to mind.

White flour consumption doesn’t seem to be making people live longer; nor does plant food consumption in general. For white flour, it is quite the opposite. Plant food consumption reflects white flour consumption to a certain extent, especially in counties where rice consumption is low. These conclusions are consistent with previous analyses using more complex statistics.

Total food is positively associated with mortality in the 35-69 range, but not the 70-79 range. This may reflect the fact that folks who reach the age of 70 tend to naturally eat in moderation, so you don’t see wide variations in food consumption among those folks.

Eating in moderation does not mean practicing severe calorie restriction. This post suggests that calorie restriction doesn't seem to be associated with increased longevity in this sample. Eating well, but not too much, is.

The bar for rice (consumption) on the left chart is likely a mirror reflection of the white flour consumption, so it may appear to be good in the 35-69 range simply because it reflects reduced white flour consumption in that range.

Green vegetables seem to be good when you consider the 35-69 range, but not the 70-79 range.

Neither rice nor green vegetables seem to be bad either. For overall longevity they may well be neutral, with the benefits likely coming from their replacement of white flour in the diet.

Dietary fat seems protective overall, particularly together with animal foods in the 70-79 range. This may simply reflect a delayed protective effect of animal fat and protein consumption.

The protective effect of dietary fat becomes clear when we look at the relationship between carbohydrate calories and fat calories. Their correlation is -.957, which essentially means that carbohydrate intake seriously displaces fat intake.

Carbohydrates themselves may not be the problem, even if coming from high glycemic foods (except wheat flour, apparently). This post shows that they are relatively benign if coming from high glycemic rice, even at high intakes of 206 to 412 g/day. The problem seems to be caused by carbohydrates displacing nutrient-dense animal foods.

Interestingly, rice does not displace animal foods or fat in the diet. It is positively correlated with them. Wheat flour, on the other hand, displaces those foods. Wheat flour is negatively and somewhat strongly correlated with consumption of animal foods, as well as with animal fat and protein.

There are certainly several delayed effects here, which may be distorting the results somewhat.  Degenerative diseases don’t develop fast and kill folks right away. They often require many years of eating and doing the wrong things to be fatal.

HealthCorrelator for Excel (HCE) is now publicly available for download and use on a free trial basis. For those users who decide to buy it after trying, licenses are available for individuals and organizations. If you are a gym member, consider asking your gym to buy an organizational site license; this would allow the gym to distribute individual licenses at no cost to you and your colleagues.

HCE is a user-friendly Excel-based software that unveils important associations among health variables at the click of a button. Here are some of its main features:

- Easy to use yet powerful health management software.

- Estimates associations among any number of health variables.

- Automatically orders associations by decreasing absolute strength.

- Graphs relationships between pairs of health variables, for all possible combinations.

The beta testing was successfully completed, with fairly positive results. (Thank you beta testers!) Among beta testers were Mac users. The main request from beta testers was for more illustrative material on how to use HCE for specific purposes, such as losing body fat or managing blood glucose levels. This will be coming in the future in the form of posts and linked material.

To download a free trial version, good for 30 use sessions (which is quite a lot!), please visit the HealthCorrelator.com web site. There you will also find the software’s User Manual and various links to demo YouTube videos. You can also download sample datasets to try the software’s main features.

This call is closed. Beta testing has been successfully completed. HealthCorrelator for Excel (HCE) is now publicly available for download and use on a free trial basis. For those users who decide to buy it after trying, licenses are available for individuals and organizations.

To download a free trial version – as well as get the User Manual, view demo YouTube videos, and download and try sample datasets – visit the HealthCorrelator.com web site.

Most human traits (e.g., body fat percentage, blood pressure, propensity toward depression) are influenced by our genes; some more than others. The vast majority of traits are also influenced by environmental factors, the “nurture” part of the “nature-nurture” equation. Very few traits are “innate”, such as blood type.

This means that manipulating environmental factors, such as diet and lifestyle, can strongly influence how the traits are finally expressed in humans. But each individual tends to respond differently to diet and lifestyle changes, because each individual is unique in terms of his or her combination of “nature” and “nurture”. Even identical twins are different in that respect.

When plotted, traits that are influenced by our genes are distributed along a bell-shaped curve. For example, a trait like body fat percentage, when measured in a population of 1000 individuals, will yield a distribution of values that will look like a bell-shaped distribution. This type of distribution is also known in statistics as a “normal” distribution.

Why is that?

The additive effect of genes and the bell curve

The reason is purely mathematical. A measurable trait, like body fat percentage, is usually influenced by several genes. (Sometimes individual genes have a very marked effect, as in genes that “switch on or off” other genes.) Those genes appear at random in a population, and their various combinations spread in response to selection pressures. Selection pressures usually cause a narrowing of the bell-shaped curve distributions of traits in populations.

The genes interact with environmental influences, which also have a certain degree of randomness. The result is a massive combined randomness. It is this massive randomness that leads to the bell-curve distribution. The bell curve itself is not random at all, which is a fascinating aspect of this phenomenon. From “chaos” comes “order”. A bell curve is a well-defined curve that is associated with a function, the probability density function.

The underlying mathematical reason for the bell shape is the central limit theorem. The genes are combined in different individuals as combinations of alleles, where each allele is a variation (or mutation) of a gene. An allele set, for genes in different locations of the human DNA, forms a particular allele combination, called a genotype. The alleles combine their effects, usually in an additive fashion, to influence a trait.

Here is a simple illustration. Let us say one generates 1000 random variables, each storing 10 random values going from 0 to 1. Then the values stored in each of the 1000 random variables are added. This mimics the additive effect of 10 genes with random allele combinations. The result are numbers ranging from 1 to 10, in a population of 1000 individuals; each number is analogous to an allele combination. The resulting histogram, which plots the frequency of each allele combination (or genotype) in the population, is shown on the figure bellow. Each allele configuration will “push for” a particular trait range, making the trait distribution also have the same bell-shaped form.


The bell curve, research studies, and what they mean for you

Studies of the effects of diet and exercise on health variables usually report their results in terms of average responses in a group of participants. Frequently two groups are used, one control and one treatment. For example, in a diet-related study the control group may follow the Standard American Diet, and the treatment group may follow a low carbohydrate diet.

However, you are not the average person; the average person is an abstraction. Research on bell curve distributions tells us that there is about a 68 percentage chance that you will fall within a 1 standard deviation from the average, to the left or the right of the “middle” of the bell curve. Still, even a 0.5 standard deviation above the average is not the average. And, there is approximately a 32 percent chance that you will not be within the larger -1 to 1 standard deviation range. If this is the case, the average results reported may be close to irrelevant for you.

Average results reported in studies are a good starting point for people who are similar to the studies’ participants. But you need to generate your own data, with the goal of “knowing yourself through numbers” by progressively analyzing it. This is akin to building a “numeric diary”. It is not exactly an “N=1” experiment, as some like to say, because you can generate multiple data points (e.g., N=200) on how your body alone responds to diet and lifestyle changes over time.

HealthCorrelator for Excel (HCE)

I think I have finally been able to develop a software tool that can help people do that. I have been using it myself for years, initially as a prototype. You can see the results of my transformation on this post. The challenge for me was to generate a tool that was simple enough to use, and yet powerful enough to give people good insights on what is going on with their body.

The software tool is called HealthCorrelator for Excel (HCE). It runs on Excel, and generates coefficients of association (correlations, which range from -1 to 1) among variables and graphs at the click of a button.

This 5-minute YouTube video shows how the software works in general, and this 10-minute video goes into more detail on how the software can be used to manage a specific health variable. These two videos build on a very small sample dataset, and their focus is on HDL cholesterol management. Nevertheless, the software can be used in the management of just about any health-related variable – e.g., blood glucose, triglycerides, muscle strength, muscle mass, depression episodes etc.

You have to enter data about yourself, and then the software will generate coefficients of association and graphs at the click of a button. As you can see from the videos above, it is very simple. The interpretation of the results is straightforward in most cases, and a bit more complicated in a smaller number of cases. Some results will probably surprise users, and their doctors.

For example, a user who is a patient may be able to show to a doctor that, in the user’s specific case, a diet change influences a particular variable (e.g., triglycerides) much more strongly than a prescription drug or a supplement. More posts will be coming in the future on this blog about these and other related issues.