Monday, July 28, 2014

Comparing Nutrient Density using the USDA Nutrient Database

In an earlier blog post I compared food groups using the NUTTAB nutrient database.  This time I’m going to do the same using the USDA nutrient database.  As usual, the group medians for micronutrients and LCO3 is the amount of a given nutrient in 2000 calories of food divided by the RDI.  This table is arranged by nutrient density (left = most, right = least).  I omitted things like restaurant foods, fast food, snack food and fats and oils as this is a comparison between whole foods

One of the problems of comparing food groups this way is that NUTTAB and the USDA ND group many types of food in the same category.  This is quite evident in the USDA ND as many of their groups are groups of food products.  So the worse whole food groups may simply have their nutrients more diluted by additives.  Finally, I did the same thing, this time using my old nutrient database which only includes some whole (and raw) foods from the USDA ND

While none of these are perfect measures, the rankings are pretty consistent between all three measures (see below) (also see the NUTTAB comparison).  My main internal debate is whether to make a new tier for nuts and put fruit, whole grains and seeds in tier 5 

Tier 1
Offal, shellfish, non-starchy vegetables, immature legumes
Tier 2
Tier 3
Meats and fish, mature legumes
Tier 4
Dairy, fruit, whole grains, seeds
Tier 5

* In NUTTAB and the USDA ND, the dairy, fruit and grains groups are most likely to be an underestimate as these groups seem to have more processed items (for example: butter, cream, yoghurt and milk with added sugar, refined grains, grain products, fruit juice)

Sunday, July 20, 2014

Nutrient Database (USDA)

I previously used some entries in the USDA nutrient database and organised them in such a way to compare the nutrient density of foods and food groups.  More recently I did a similar thing but used all the entries in the NUTTAB nutrient database (Australia) to form another nutrient database.  This time I used all (8463) the entries in the USDA database

To download the nutrient database click here.  Don't try and read it in the Google drive viewer because you won't be able to see much of it, just download it

Sunday, July 13, 2014

Comparing Nutrient Density using the NUTTAB Nutrient Database

One of the main purposes of the nutrient database was to compare the nutrient content of foods and food groups in a meaningful way - the amount of nutrients in 2000 kcal of food divided by the RDI.  All values in this post are based on that measure 

Whole Foods are Superior 

I firstly compared categorised the groups into whole foods, SAD meals, junk foods and extras*.  It’s clear from this that whole foods on average are superior and good sources of almost all nutrients, such that simply eating a balanced whole food diet is very likely to fulfil almost** all nutrient needs and more.  There’s something to be said for ‘just eat real food’ (JERF) 

In this very rough measure, the whole food average was only lacking in fluoride (which you get in the water supply), manganese, sodium (which you can easily add to foods) and vitamin A.  Manganese is mostly found in plant foods, particularly grains.  The AI is based on the median population intake and so I think our grain-based diet skews estimates on how much manganese we need.  Vitamin A is kind of surprising, but it’s really only found in sufficient quantities in vegetables, eggs, liver, dairy and some fruits; where certain vegetables and liver are extremely good sources of vitamin A.  This might be just because I took the medians of groups rather than the mean 

The weight and macronutrients are also interesting.  Whole foods are generally high in protein and less calorie dense than SAD meals and junk food, and the SAD meals is remarkably close (by accident) to the macronutrient ratio eaten by Australians (~17:33:45).  There’s also like a gradient where protein decreases and energy density increases as foods/meals get more processed with increasing amounts of added refined sugars, starches and fats.  That being said, the averaged protein in the whole foods category is too high
* Whole foods included: dairy (average of cheese and milk), eggs, fruit, aboriginal plant foods, legumes, offal, nuts and seeds, crustacea and molluscs, vegetables and meat (which is an average of beef, game and other meat, lamb, mutton, pork, poultry, veal and fish) 

SAD meals included: bread and bread products, breakfast cereals, flours, grains and starches, hamburgers, pizza and other takeaway products, noodles and pasta, dairy and meat alternatives, processed meats, asian restaurant foods, mediterranean restaurant foods, processed fish, crustacea and molluscs and soups 

Junk foods included: biscuits, cakes, slices and other battered products, pastries, pies and tarts, ice cream & edible ice products, yoghurts and dairy desserts, snack foods, chocolate based and sugar based confectionary 

NOTE: I averaged the meats together and dairy together because there were several groups of meat that would otherwise skew the average and also by averaging the groups of meat and dairy into two groups it resulted in 5 groups of animal foods and 5 groups of plant foods.  Within these groups: ‘milk’ was more nutrient dense than ‘cheese’; and ‘game and other meat’, ‘veal’ and ‘fish’ were more nutrient dense than other meats 

** There are some nutrients that aren’t widely found in foods in sufficient quantities, such as choline/betaine 

Which Whole Foods are More Nutrient Dense? 

By using the nutrient database we can also get an idea as to which food groups are more nutrient dense, which you can see in the table below arranged from most nutrient dense to least
Obviously there are problems with calculating nutrient density this way:
  • In the USDA nutrient database the same measurements was higher but particularly for vegetables (3.5) and fruits (1.24), which seems to be due to a lot of 0’s in the raw data where you would expect there to be something
  • Values like 106.90 for B12 in offal that inflates the average, although in this comparison those really high values didn’t make much difference in the ranking (except aboriginal plant foods, see first point)
  • This doesn’t account for some missing micronutrients (choline/betaine, K1, K2), bioavailability and other nutrients/beneficial compounds in foods

Tuesday, July 8, 2014

The Chowdhury Meta-Analysis

In March this year, another meta-analysis (by Chowdhury, et al) was published that looked at the relationship between fats and coronary heart disease (CHD) [1].  Its conclusions were that Current evidence does not clearly support cardiovascular guidelines that encourage high consumption of polyunsaturated fatty acids and low consumption of total saturated fats”.  As expected, the mainstream was very critical of the paper, but mainly regarding the inclusion/interpretation of observational studies and those related to omega 3 supplementation.  Anyway, I’m going to ignore all that and focus on their interpretation of the clinical trials that replace SFA with omega 6 PUFA 

They included randomised controlled trials with 50 or more total coronary outcomes.  This criterion excluded the unfavourable Rose Corn Oil Trial (18 events), but didn’t exclude the favourable STARS trial (7 by their assessment).  Not like that matters much if you’re simply going to do a quantitative assessment as these two trials are too small to have a noticeable effect on the outcome (a relative risk (RR) assessment).  Their description of the trials they included and the risk of bias assessments are shown below (click to enlarge)

*Mixed poly-unsaturated intervention with linoleic acid as the primary fatty acid
Note: the age of the men in the FMHS was 34-54 (not 34-44)
NFMI = non-fatal myocardial infarction; FMI = fatal myocardial infarction; FCHD = fatal coronary heart disease; SCD = sudden cardiac death
Based on the trials, they calculated the RR for omega 6 supplementation to be 0.89 with a confidence interval of 0.71 to 1.12, making it not statistically significant.  But when they excluded the Sydney Diet Heart Study (SDHS) in a sensitivity analysis the RR was 0.81, which was significant

There are some problems with the meta-analysis: 

Under omega 6 supplementation, they included trials that did much more than simply replace SFA with omega 6.  The Oslo Diet Heart Study, Finnish Mental Hospital Study (FMHS) and STARS are the best examples of this as there were many other differences between the control group and the high omega 6 group that were almost always favorable to the high omega 6 group (such as less trans fats, more omega 3, fruit and vegetables, weight loss, etc).  But you also have other trials like the LA Veterans Administration Trial, which was mostly well done, except the control group was eating reheated butter as a main fat source and consequently had an insufficient intake of vitamin E (2.6 mg and the RDI is 10 mg) 

They only reported CHD outcomes and didn’t include total mortality.  Previous meta-analyses by Hooper, et al and Mozaffarian, et al suggest some benefit for CHD, but find no difference in total mortality.  What good are these interventions if CHD mortality decreases, but total mortality stays the same? 

Supportive of DHH for CHD Events
Supportive of DHH for CHD Mortality
Supportive of DHH for Total Mortality
Hooper (Cochrane)
RR = 0.82
CI = 0.66 to 1.02
RR = 0.92
CI = 0.73 to 1.15
RR = 1.02
CI = 0.88 to 1.18
(+5% PUFA)
RR = 0.81
CI = 0.70–0.95
RR = 0.80
CI = 0.65–0.98
RR = 0.98
CI = 0.89–1.08
* Significant difference 

Ultimately the problem with all the meta-analyses of this type is that they treat the trials as if they’re same, ignore the other differences between the groups and the overall quality of the trial, and then just run some statistics.  When you have such (mostly) poor and variable trials (with no further ones being done) I think a qualitative approach is the best way to discuss these trials and arrive at a conclusion 

Walter Willett made a similar point (if only he tell this to his colleagues): 

The controversy should serve as a warning about meta-analyses, Willett adds. Such studies compile the data from many individual studies to get a clearer result. "It looks like a sweeping summary of all the data, so it gets a lot of attention," Willett says. "But these days meta-analyses are often done by people who are not familiar with a field, who don't have the primary data or don't make the effort to get it." And while drug trials are often very similar in design, making it easy to combine their results, nutritional studies vary widely in the way they are set up. "Often the strengths and weaknesses of individual studies get lost," Willett says. "It's dangerous." [2]