Recently, there have seen several interesting articles finding that life expectancy increases for a while as you BMI increase beyond the ideal, and then decrease. This is very old news for me, as a statistician, I have seen this in actuary tables since the 1980’s (tables used to determine insurance premiums — an industry where statistics trumps beliefs, usually).
Body mass index is defined as the individual’s body mass divided by the square of his or her height. As a physicist, I can’t help but notice that we happen to live in at least a three dimensional world (adding time, makes it four dimensional). Additionally, politics have had it impact by changing the levels of overweight based on belief or corporate agenda (i.e. if more people are overweight or obese, then there is more opportunity to prescribe or sell items to fix what may be a non-existent issue). For more back ground, see this Wikipedia article:
The problem of the non-actuary approach is that while you may find increased risk of heart disease, high blood pressure, stroke, diabetes; decreased risks of other diseases are not considered. In other words — a blinkered view.
If we assume that BMI 25.is ideal for a 1.6 meter individual, we find a weight of 72 kg. If we instead use BMI-3 or Mass/ (height x height x height), the equivalent number would be 14.655. For a 1.5 m person, the same BMI-3 suggests a weight of 49kg. For a 2 m person, BMI-3 suggests 136 kg.
|Height||BMI = 25||BMI-3 = 14.655|
|1.5 m||57 kg||49 kg|
|1.7 m||72 kg||72 kg|
|2.0 m||111 kg||136 kg|
In other words, shorter people can carry 16% more weight, and tall people must be 20% leaner. So on BMI, I would come in as 30, but with BMI-3, it’s 15.8. Or 30/25 = 20% above with BMI versus 8% above with BMI-3.
The term “evidence based science” is often tossed around, the unfortunate termed that is missing is “biased”, i.e. “biased evidence based science”. The majority of studies are not reviewed by hard-core independent statisticians.
In terms of studies for CFS and chronic lyme, studies that found no benefit from antibiotics often suffer from this type of bias. Antibiotics are part of a complex regime when remission has been reported — taking them in isolation does not “prove” they do not work — it shows only that they do not work when applied in a naive manner.