WEBVTT

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Prof: Let's talk about
biology and eating,

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an incredibly interesting yet
complicated topic.

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By the time we're done with
this class I hope that you,

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as I am, will be impressed with
just how many factors are

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affecting what people choose to
eat,

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how much they eat,
the way they regulate their

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body weight,
intake of certain nutrients,

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and the like.

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There are all these
interesting, some cases

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redundant systems that are
affecting what people eat.

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Then we're going to take the
completely opposite attack next

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week where--in the next class on
Monday,

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we're going to begin by talking
about addiction,

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which is really a carry over
from this,

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food and addiction,
but there's just not enough

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time to cram it into this
lecture,

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and then we're going to move
onto culture and how culture

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affects eating.

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You could make a strong
argument that eating is driven

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almost entirely by biology,
or you could also make the

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argument that it's driven almost
entirely by culture;

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but where it gets extremely
interesting is where the two

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intersect and how the two affect
one another.

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We'll talk about more of the
biology today.

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Some years ago I was talking to
a person who worked for M&M

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Mars,
a big candy company of course,

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and two of their biggest
selling products are Three

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Musketeers and Snickers candy
bars.

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This individual told me that
they're--that one of them is

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preferred much more by adults
and the other preferred much

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more by children and there are
properties that I'll ask you

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about in a minute that
distinguish why one is more

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attractive to children and the
other by adults.

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Let's try out the clickers
today.

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Which do you think more
attractive to the kids:

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the Three Musketeers--oh you
know what I need to do?

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I'm sorry.

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I need to boot this up through
another program.

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I apologize.

01:48.780 --> 01:56.340


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See I was so worried that we
had so much to do today that I

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forgot to boot this up.

02:00.230 --> 02:33.240


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Okay, this time it should work.

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Now you've had a chance to
think about it as well.

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All right, so let's see if the
technology is correct.

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What do kids prefer:
the Three Musketeers or the

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Snickers?

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Go ahead and log in,
make sure the green light goes

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on.

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Okay so people are still voting
it appears.

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All right, so let's stop the
voting now and see if this--yeah

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so 49% of you said Three
Musketeers, 51% said Snickers,

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so almost a dead heat there.

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Well the answer is the kids
prefer the Three Musketeers,

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adults prefer the Snickers,
who can guess why that might

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be?

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Yes.

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Student:
>

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Prof: All right,
you got the right answer that

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it's the peanuts that make the
difference.

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What is it about the peanuts do
you think make adults like it

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and kids not?

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I mean kids like peanut butter
after all.

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Texture is the answer.

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The peanuts introduce a texture
like a Nestle's Crunch Bar would

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have.

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Kids tend to like uniformity in
texture and tend not to like

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extra things thrown in that
confuse them with the texture,

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whereas, adults do like things
that have texture,

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so as much as the adults may
like the taste of the peanuts in

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that kind of a thing,
it's really the texture that

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makes them like that particular
candy bar.

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So that's one little
interesting little sensory

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property of foods that will
affect who likes what.

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Today we're going to talk about
how biology affects a number of

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things and these are very
interesting questions:

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how does biology affect taste?

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Which foods do we prefer or not?

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We've talked about that some
extent before.

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How does it affect mood?

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People seek out foods when
they're feeling depressed,

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when they're feeling anxious,
when they're feeling lonely.

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It's different for some people;
how is biology affecting that?

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Then of course,
these other things are very

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important as well.

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Just to make a brief
distinction on the third bullet

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point about hunger and satiety,
you probably know what those

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terms mean.

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Hunger is pretty obvious,
satiety refers to how full a

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person feels and when eating
shuts off once it starts.

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We'll come back and talk about
some animal studies that

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distinguish hunger and satiety,
human studies as well.

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Today we're going to talk about
taste and I'm going to introduce

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this concept that was developed
by a former Yale researcher

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named Linda Bartoshuk on
supertasting.

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We're going to talk about the
physiology of eating,

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or what the animal researchers
call feeding.

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I'm not sure why it's feeding
in animals, and eating in

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humans, but that's the way it
is.

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We'll talk about energy balance
in the body and how body weight

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gets affected,
the genetics of these things,

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and then we'll end with this
discussion of something

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interesting called taste
aversions.

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First of all, taste.

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There's sort of this basic
Biology 101 that--where people

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get a sense that there are
different sensory properties of

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taste and different parts of the
tongue that perceive these

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tastes.

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This has been known for years
and it's easy to map out the

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biology of this kind of thing.

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This is a pretty simplistic
version of it,

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but there's some very
interesting work that's taken

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this to a new level.

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You can see how taste effects
has such a profound effect on

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people, and what it does to
them, and you see it in facial

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expression.

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You see somebody eating
something sweet,

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fatty, that they enjoy and you
get a look like that.

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Somebody eats something they
don't like and you get a look

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that.

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And so if you just saw this
face on the right and you asked

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people to say,
well what do you think is

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happening with this man?

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People could say well he saw
something disgusting or he

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tasted something disgusting,
and that word disgust is pretty

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interesting in this context,
but it shows up in facial

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expressions.

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Now here's the concept of
supertasting.

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This researcher Linda
Bartoshuk, a terrific scientist,

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who was here at Yale for many
years and then moved to The

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University of Florida,
has looked at the way the

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tongue and the body perceives
tastes of foods.

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Her desire, one of her
scientific desires,

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is to map out the biology of
these things and to understand

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how taste affects biology and
how biology affects taste.

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She's really,
as I said, a very fine

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researcher.

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Well one of the things that
she's done is work with

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something called PROP,
and PROP is the abbreviation

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for this compound that you see
on the bottom,

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Propylthiouracil.

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It got the--which is a
variation of something that got

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discovered in 1931 by a chemist
named Fox.

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He was working with that
particular compound that you see

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in the top bullet point there,
and it exploded in his lab.

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What happened is he found that
the associate got some of this

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in his mouth,
tasted it as quite bitter,

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but he didn't taste it at all,
and he got interested in why

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would this be this case?

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Why would the two of them have
the same thing in their mouth,

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but taste it much differently?

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It wasn't even so much that one
liked it and the other one

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didn't;
one of them couldn't even

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detect it.

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That got picked up by
scientists, including Bartoshuk,

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who started looking at this and
have this used this compound,

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the PROP, in many,
many interesting studies.

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What they do is they put a
little bit of this on a little

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piece of paper and you put the
paper on your tongue and keep it

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in your mouth for just a short
time.

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The population breaks down into
three groups.

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There are groups who can't even
detect it at all,

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they're called the non-tasters.

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There are some people who
experience it as mildly bitter;

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those are the medium tasters.

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And then the supertasters are
people who experience it as

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extremely bitter.

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The population breaks down
about like this:

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quarter of the people are at
the extremes and 50% of people

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in the middle.

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The interesting comparison is
between the non-tasters and the

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supertasters.

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What Bartoshuk has done is
mapped out the tongue physiology

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on this,
and there's a part of the

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tongue shown in this little
picture here,

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of where there's a high density
of these things called fungiform

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papilla.

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Bartoshuk and others have shown
that the ability to detect this

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particular chemical resides in
that part of the tongue.

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What you do is just put a
little dye on the tongue and

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then you take a picture of it,
and you can actually count,

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once it's magnified,
the number of these fungiform

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papilla.

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If you look at the picture,
but the comparison of tasters

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and non-tasters,
you get something like this.

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So at that part of the tongue
each of those white dots would

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represent one of the fungiform
papilla.

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The supertasters--again the
ones who detect that chemical as

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very bitter--look like that,
so really quite a difference.

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You get this interesting sense
between 25% of the population

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looks like that,
25% looks like the top slide

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and you could see how people--to
the extent this is related to

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food preferences--how the tongue
people are born with,

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and there's probably related
physiology elsewhere in the body

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to that,
how that's going to affect what

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people eat.

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Now what she--what Linda
Bartoshuk has found is that the

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people who were the supertasters
in that 25% group,

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and to a lesser extent but
still true,

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of that 50% in the middle,
the larger group,

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they perceive an intense burn
from things that are considered

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food irritants and they also
perceive the most intense

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sweetness from sweet foods.

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Bitter vegetables,
like broccoli and cauliflower

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for example, are experienced as
very bitter by that 25% of the

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population, the supertasters.

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The non-tasters don't
experience them as bitter or

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difficult at all.

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You can imagine how easy it is
for the non-tasters to eat those

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kinds of things and how
difficult it is for the

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supertasters to eat those,
and it also has relationships

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to desire for fat.

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The sweetness is interesting
too.

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If Linda were here doing a
demonstration,

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she would have you all put this
on your tongue,

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see which group you happen to
fall in,

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give you a little butterscotch
candy,

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and then have you rate how
sweet it is and how pleasant

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that taste is.

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She could predict in advance
who would experience it as very

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sweet and very pleasant,
depending on whether you were a

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supertaster or not,
with the supertasters

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experiencing the sweetness as
very desirable and very powerful

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sensory experience.

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Coupled together is this
turn-off to the irritants,

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the fresh fruits and
vegetables, not fruits so much

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because they're sweet but to the
bitter vegetables,

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and then a liking for sweet
things.

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Of course this would affect
dietary preferences and

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therefore health.

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This is a very interesting
phenomenon that she has found.

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The way she puts it in more
generic terms is this:

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that the supertasters live
on--in this neon world of taste

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where everything is bright and
vivid, vivid and bold and

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potent;
whereas the non-tasters live in

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this pastel world where they're
not experiencing as much

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preference for sweets nor the
aversion to the bitter

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vegetables and things.

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And so you would expect from
that point of view for the

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supertasters to have greater
problem with weight,

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greater problems with diet
related to health than is the

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case with the non-tasters.

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That begins to give you a sense
of what's involved just with the

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taste part of the equation.

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Now let's talk about the
physiology of eating.

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Now, I'm going to break this
down for you.

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It took me a lot of time to
uncouple all the little pieces

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in this graph,
but I'm going to show it to you

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piece by piece.

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This is one conceptualization
of all the factors that are

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affecting taste and affecting
what people eat.

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Central to this are central
signals those--they would be the

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brain and the central nervous
system and then over here are

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peripheral signals that happen
outside the brain,

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mainly from the body's organs.

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Let's break this down and take
it piece by piece and show you

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how it might all fall together.

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Let's start with the brain.

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First of all,
the brain and its subsequent

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physiological reaction is
affected by external factors.

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The fact that you might seek
out certain foods to soothe

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yourself when you're stressed
means that there are external

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things going on that make
certain foods more desirable to

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you at some times then others,
that would be an example.

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There are other things like
what's going on in the

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environment,
as we used as an example

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before, you just had a nice meal
at a restaurant,

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you don't feel like you could
eat one more calorie,

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here comes the dessert cart and
all of sudden you're hungry

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again;
those are external factors that

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affect the brain and therefore
the physiology.

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There are central signals and
peripheral signals,

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as I mentioned before.

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Let's look at the central
signals first.

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There are signals that are
going on in the brain,

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some of which stimulate eating
and others inhibit eating,

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and that's the hunger and
satiety that we mentioned a

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moment ago.

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These things affect--directly
affect food intake.

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How much food intake--what
people eat and what kind of

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foods they eat,
have a feedback loop to the

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brain that affects subsequent
intake, so you get this loop

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going on.

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From a common sense point of
view, if somebody eats a lot of

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food, then the feedback loop
would go to the brain,

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I've had enough it's time to
stop.

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There are signals--or I haven't
had enough and I need to eat

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more and these central signals
that stimulate or inhibit eating

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are--fall into that category.

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There are certain number of
chemicals in the body that

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stimulate eating,
certain ones that decrease

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eating, and I'll just point out
a few of these.

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You don't really need to know
what these are but I'll point

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out just a few as examples.

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One would be the NPY which
stands for Neuropeptide Y,

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and the 5HT which is something
that you will hear more of in

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the common language as
serotonin.

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It's very interesting how these
two things affect--affect

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eating,
and the SSRI is the selective

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serotonin reuptake inhibitors
are a class of drugs mainly used

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for depression.

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Prozac and drugs like that are
SSRI drugs.

15:00.460 --> 15:03.240
Those drugs,
once they were discovered,

15:03.240 --> 15:06.690
the laboratory scientists found
in the initial animal studies

15:06.692 --> 15:10.092
just looking at whether these
drugs were safe or not for the

15:10.086 --> 15:12.996
health of the animals,
found out that the animals lost

15:12.996 --> 15:15.226
weight and there was some
suppression of appetite.

15:15.230 --> 15:18.100
Then scientists said,
well boy if you think there's a

15:18.096 --> 15:21.016
market for depression,
just think about the market for

15:21.018 --> 15:21.898
weight loss.

15:21.899 --> 15:24.889
There was some marketing of
these drugs for that at one

15:24.886 --> 15:27.816
point but they haven't really
worked out that well.

15:27.820 --> 15:31.990
You can see how these things
are affecting food intake in

15:31.993 --> 15:34.233
feedback loops to the brain.

15:34.230 --> 15:37.660
People eat, how much they eat
is determined partly by some of

15:37.663 --> 15:40.203
these things,
what they eat then affects

15:40.202 --> 15:42.482
those things,
and you get the feedback loop

15:42.477 --> 15:42.937
going on.

15:42.940 --> 15:47.550
There's this--what happens
between what people eat and then

15:47.548 --> 15:50.198
subsequently,
what their brain tells them to

15:50.198 --> 15:53.048
eat is a very interesting part
of the equation and that's where

15:53.048 --> 15:55.208
the--some of the peripheral
signals come in.

15:55.210 --> 15:57.790
Your body takes in food,
it senses that you've had

15:57.785 --> 15:59.675
nutrients of one sort or
another,

15:59.678 --> 16:02.668
a series of metabolic processes
kick in,

16:02.668 --> 16:05.798
and those affect what happens
to the brain subsequently.

16:05.798 --> 16:07.768
Here's some of the things that
fall in those,

16:07.765 --> 16:09.325
those are the peripheral
signals.

16:09.330 --> 16:14.710
These are some of the compounds
that determine--that are the

16:14.712 --> 16:16.632
peripheral signals.

16:16.629 --> 16:20.079
We talked about blood glucose
last week when we--or on Monday

16:20.077 --> 16:22.487
when we talked about the
glycemic index.

16:22.490 --> 16:24.740
You eat food,
your blood sugar goes up,

16:24.739 --> 16:28.469
that's the blood glucose goes
up, and that affects the brain.

16:28.470 --> 16:31.760
We talked about hunger and
there was the spike with the

16:31.764 --> 16:34.814
high glycemic index foods,
the lower spike with low

16:34.813 --> 16:38.173
glycemic index foods;
so the glucose that you see

16:38.168 --> 16:40.468
over here is one example of
that.

16:40.470 --> 16:44.090
Insulin is also very involved,
as you might imagine,

16:44.090 --> 16:47.350
and then hormones that get
talked about more in the press,

16:47.350 --> 16:50.740
Ghrelin and Leptin are
important players here,

16:50.740 --> 16:52.100
and then Cortisol which is a
stress hormone,

16:52.096 --> 16:52.786
is a player as well.

16:52.789 --> 16:58.089
These act through organs.

16:58.090 --> 17:01.410
It's the effect of food on
different organ systems in the

17:01.413 --> 17:05.333
body that create the release or
the suppression of these things.

17:05.328 --> 17:08.848
First the liver is involved in
the glucose, so food intake will

17:08.851 --> 17:12.261
affect the liver that will
determine blood glucose levels.

17:12.259 --> 17:16.299
You have the gastrointestinal
tract involved as well,

17:16.298 --> 17:20.188
so of course your small and
large intestine in the stomach

17:20.193 --> 17:22.463
are involved here,
and those will affect these

17:22.463 --> 17:23.703
things, especially insulin and
Ghrelin.

17:23.700 --> 17:28.250
Then you have the adipose
tissue.

17:28.250 --> 17:31.170
The fat cells are involved
because they're perceiving

17:31.173 --> 17:34.833
signals from the incoming food;
those affect Leptin.

17:34.828 --> 17:38.578
Then you have adrenal glands
that affect the Cortisol.

17:38.578 --> 17:42.948
Cortisol, a stress hormone,
is an interesting player here.

17:42.950 --> 17:45.150
One of your teaching fellows,
Alli Crum,

17:45.150 --> 17:47.850
will be talking about some of
the work that she's doing in a

17:47.853 --> 17:50.693
later class and she's doing some
work that involves a number of

17:50.694 --> 17:53.054
these things,
a very interesting study that

17:53.047 --> 17:56.047
looks at the intersection of
psychology and perceptions in

17:56.049 --> 17:56.629
biology.

17:56.630 --> 18:01.010
These things then have signals
that would either increase or

18:01.005 --> 18:04.485
decrease eating,
and they feed back to the brain

18:04.490 --> 18:06.270
in that kind of way.

18:06.269 --> 18:08.709
So you put all this system
together and you get things like

18:08.713 --> 18:09.013
this.

18:09.009 --> 18:12.629
This is what happens after food
is consumed and this--then it

18:12.630 --> 18:16.190
goes back to the brain and the
brain--then that affects what

18:16.190 --> 18:17.880
people eat subsequently.

18:17.880 --> 18:21.060
If you put it all together you
have this, what seems like

18:21.060 --> 18:23.050
perplexing picture to begin
with.

18:23.048 --> 18:26.128
It still might be perplexing
but the--what I'm hoping that

18:26.132 --> 18:29.382
you take away from this is an
appreciation for how many parts

18:29.375 --> 18:32.075
of the body are affected when
people eat food;

18:32.078 --> 18:35.428
how the brain is an important
player and what people choose to

18:35.426 --> 18:38.126
eat in the beginning;
and how all these feedback

18:38.133 --> 18:39.303
loops are occurring.

18:39.298 --> 18:43.148
It's even more complicated then
this, but this is an interesting

18:43.151 --> 18:45.231
start at looking at the picture.

18:45.230 --> 18:49.860
Let's look at some factors that
affect short-term food intake.

18:49.858 --> 18:52.718
Now long term food intake,
how much people are eating

18:52.723 --> 18:55.713
overall,
is affected by some overlapping

18:55.705 --> 18:59.065
but also different factors,
but let's talk about what

18:59.071 --> 19:02.321
people eat in the short term,
like in a given meal at a given

19:02.316 --> 19:03.196
point in time.

19:03.200 --> 19:07.030
There are different ways
scientists have looked at this

19:07.025 --> 19:11.275
and they've measured different
patterns of what people eat by

19:11.276 --> 19:14.036
breaking things down into this
way.

19:14.038 --> 19:17.688
What factors determine whether
a meal gets started?

19:17.690 --> 19:22.280
What determines whether you
start eating at a given point?

19:22.279 --> 19:24.029
When do you become hungry?

19:24.028 --> 19:28.308
What triggers some awareness in
your mind that you're ready to

19:28.308 --> 19:28.728
eat?

19:28.730 --> 19:32.350
That would determine meal
initiation or snack initiation,

19:32.346 --> 19:33.246
or whatever.

19:33.250 --> 19:36.370
The size of the meal of course
is an important factor,

19:36.368 --> 19:40.398
and there are real differences
here between the meal initiation

19:40.395 --> 19:43.385
and the meal size,
and then the meal termination

19:43.387 --> 19:44.677
comes in here as well.

19:44.680 --> 19:47.330
There have been very
interesting studies over the

19:47.332 --> 19:50.812
years using different drugs with
animals to look at what it does

19:50.814 --> 19:52.034
to their appetite.

19:52.029 --> 19:55.339
Of course the pharmaceutical
companies have done thousands

19:55.343 --> 19:58.773
and thousands of these studies
in hope of finding drugs that

19:58.773 --> 20:02.033
will help people with their
eating and their weight.

20:02.028 --> 20:05.578
When you do studies with
animals you find that some drugs

20:05.579 --> 20:09.219
affect the initiation of a meal,
they affect hunger,

20:09.217 --> 20:13.317
so how likely is it for the
animal (or a human) to start

20:13.318 --> 20:15.458
eating,
and that would be the hunger

20:15.461 --> 20:16.031
part of it.

20:16.028 --> 20:19.258
Some drugs selectively affect
that but they don't affect how

20:19.256 --> 20:21.276
much is eaten once the meal
starts.

20:21.278 --> 20:25.758
What might go up then is the
interval between meals or even

20:25.760 --> 20:29.780
how many meals are eaten,
but what's during a meal is

20:29.778 --> 20:32.018
untouched by these drugs.

20:32.019 --> 20:34.809
Then there are a whole other
class of drugs that affects

20:34.811 --> 20:35.321
satiety.

20:35.318 --> 20:38.428
The interval between meals
remains untouched;

20:38.430 --> 20:42.060
the likelihood of initiating a
meal remains untouched;

20:42.058 --> 20:45.458
but how much the organism eats
once the meal starts is highly

20:45.457 --> 20:46.757
affected by the drug.

20:46.759 --> 20:49.959
That leads us to think that
when people have problems

20:49.963 --> 20:53.613
controlling their eating,
there could be some people who

20:53.605 --> 20:57.495
have disorders of hunger--or not
disorders necessarily because

20:57.502 --> 21:00.482
that makes it too clinical;
but problems with hunger and

21:00.482 --> 21:02.272
other people might have problems
with satiety.

21:02.269 --> 21:05.139
Think in your own experience,
when there are times when you

21:05.136 --> 21:07.946
are eating things you don't
think you should eat or you're

21:07.954 --> 21:11.074
eating more then you think you
should eat, what's going on?

21:11.069 --> 21:13.539
Is it the hunger?

21:13.538 --> 21:16.198
Do you just want to eat all the
time or do you want to eat more

21:16.204 --> 21:16.554
often?

21:16.548 --> 21:19.338
Or are you eating at regular
times but you're just having

21:19.339 --> 21:22.329
trouble eating as much as you
think you should and you overdo

21:22.328 --> 21:25.068
it and there could be
combinations of these things.

21:25.068 --> 21:28.738
That's one interesting broad
cut at this whole issue.

21:28.740 --> 21:31.320
There are things that affect
all of these.

21:31.318 --> 21:34.298
For example,
hunger, the biological

21:34.301 --> 21:37.991
experience of hunger,
will affect both meal

21:37.986 --> 21:40.526
initiation and meal size.

21:40.529 --> 21:43.679
The appetite,
which is what a person's desire

21:43.683 --> 21:47.053
is for particular nutrients or
particular foods,

21:47.053 --> 21:50.283
will affect both of these
things as well.

21:50.279 --> 21:54.939
Satiety, which is the sense of
feeling full,

21:54.940 --> 21:57.450
as I mentioned,
will affect when a meal

21:57.445 --> 21:59.905
terminates,
when the next meal will start

21:59.905 --> 22:03.025
because that feeling of full
will only last so long and then

22:03.034 --> 22:06.324
of course that affect--that will
affect the meal frequency.

22:06.318 --> 22:10.128
Then there's a concept that I'm
going to explain in a minute

22:10.131 --> 22:14.071
called sensory specific satiety
that will affect meal size and

22:14.073 --> 22:15.433
meal termination.

22:15.430 --> 22:19.470
When you add all these things
up you have this array of arrows

22:19.470 --> 22:21.060
going every which way.

22:21.058 --> 22:25.368
Again, you can get a sense of
how the biology affects actual

22:25.374 --> 22:28.304
eating patterns in
these--this--this very

22:28.299 --> 22:32.689
interesting network of affects
and they come together in very

22:32.686 --> 22:34.366
interesting ways.

22:34.368 --> 22:38.508
Let me tell you what sensory
specific satiety is in a minute,

22:38.509 --> 22:41.569
but before we do that,
I'm going to have you use your

22:41.573 --> 22:44.523
clickers and to get your sense
of another issue.

22:44.519 --> 22:49.349
Here's a product called
Munchies, let's put this into a

22:49.353 --> 22:51.953
specific context of a food.

22:51.950 --> 22:55.320
Frito-Lay, I don't know how
long ago,

22:55.318 --> 22:58.968
at least a couple years or
more, introduced a line of snack

22:58.965 --> 23:02.605
products called Munchies and
there was one in particular in

23:02.612 --> 23:06.072
this chartreuse bag that was
oriented towards kids.

23:06.068 --> 23:08.818
Its Munchies Kids Mix,
and each of these Munchies

23:08.815 --> 23:12.015
products takes a series of
things that Frito-Lay may sell

23:12.017 --> 23:15.217
anyway and puts them into a
combination, into a mix.

23:15.220 --> 23:18.720
Now this one in particular,
this Munchies Kids Mix,

23:18.715 --> 23:22.905
if you look at the back of the
bag here's what's put together

23:22.911 --> 23:24.031
in this mix.

23:24.028 --> 23:28.298
It's Rold Gold pretzels,
it's Captain Crunch Cereal

23:28.300 --> 23:31.100
right under that,
it's Doritos,

23:31.104 --> 23:34.054
it's popcorn,
Smart Food Popcorn,

23:34.048 --> 23:37.608
it's Cheetos,
the cheese ball things,

23:37.608 --> 23:40.798
and then it's M&M type
candies.

23:40.798 --> 23:43.818
These are what are going into
this particular snack mix.

23:43.818 --> 23:48.078
Now, is this snack mix good or
not good for kids compared to

23:48.076 --> 23:50.886
what they might be eating
otherwise,

23:50.890 --> 23:55.040
which might just be one of
these things put together--put

23:55.044 --> 23:55.864
in a bag?

23:55.858 --> 24:01.688
I'll talk to you a little bit
about how the Munchies is being

24:01.692 --> 24:04.172
marketed,
and Frito-Lay has several

24:04.172 --> 24:07.372
different messages on the bag
that make it look like its going

24:07.373 --> 24:09.423
to be a healthier product for
kids,

24:09.420 --> 24:11.930
and that there's something,
somehow good about this

24:11.932 --> 24:13.542
compared to the normal things.

24:13.538 --> 24:18.178
Let's compare what would happen
here,

24:18.180 --> 24:22.410
so I'd like you to use your
clickers and we'll--on the next

24:22.406 --> 24:26.846
slide--to see whether you think
that if you just sat kids down

24:26.853 --> 24:31.543
in a whatever kind of a setting,
and gave them unlimited amounts

24:31.539 --> 24:34.109
of that and unlimited amounts of
that,

24:34.108 --> 24:36.108
what they would eat more of and
why?

24:36.108 --> 24:38.248
Let's see how your intuition
works here.

24:38.250 --> 24:40.720
There's something that I
mentioned in an earlier class

24:40.715 --> 24:43.085
that was kind of a tip off as to
the answer here.

24:43.088 --> 24:46.968
The question is how many of you
believe that people would eat

24:46.970 --> 24:49.100
more Munchies,
how many of you believe they

24:49.099 --> 24:51.479
would eat more Cheetos,
or how many believe it would be

24:51.479 --> 24:51.949
the same?

24:51.950 --> 24:53.760
Vote one, two, or three here.

24:53.759 --> 25:01.999


25:02.000 --> 25:05.490
Okay, a few people still
voting, more Munchies,

25:05.492 --> 25:07.622
more Cheetos,
or the same?

25:07.619 --> 25:10.279
Okay let's look at the graph;
let's see what you guys said.

25:10.278 --> 25:14.528
Okay, 78% of you said that it
was more Munchies,

25:14.530 --> 25:16.340
why would that be?

25:16.338 --> 25:21.028
Variety that was the tip off,
so I'm glad you guys remembered

25:21.031 --> 25:21.581
that.

25:21.578 --> 25:23.798
It was nice that you were
paying attention early that

25:23.801 --> 25:24.231
morning.

25:24.230 --> 25:28.490
Variety is one issue and
variety connects with this

25:28.486 --> 25:33.166
concept of sensory specific
satiety that I'd now like to

25:33.167 --> 25:34.187
explain.

25:34.190 --> 25:40.340
Sensory specific satiety is
what happens when an organism

25:40.336 --> 25:44.286
eats the same food over and
over,

25:44.288 --> 25:48.328
and basically what it means is
that the palpability or the

25:48.333 --> 25:52.593
likeability of certain foods and
desire for them will go down

25:52.588 --> 25:55.638
more than for foods that are not
eaten.

25:55.640 --> 25:59.760
Presumably, this is the
evolutionary response to the

25:59.763 --> 26:03.083
body's need for a variety of
nutrients.

26:03.078 --> 26:05.278
If you eat the same thing over
and over again,

26:05.278 --> 26:06.858
even though you might really
like it,

26:06.858 --> 26:08.468
you're only getting certain
nutrients,

26:08.470 --> 26:10.680
you're being deprived of
others, and hence,

26:10.680 --> 26:13.280
the body sends out this signal
that hey I've had enough of that

26:13.279 --> 26:15.209
stuff it's time to move onto
something else.

26:15.210 --> 26:18.890
That's the sensory specific
satiety concept and it's been

26:18.891 --> 26:20.931
studied a lot over the years.

26:20.930 --> 26:24.780
There's a researcher at Penn
State named Barbara Rolls and

26:24.775 --> 26:29.155
I'll discuss some of her work
later on in a different context,

26:29.160 --> 26:32.110
but she has done some terrific
studies on sensory specific

26:32.112 --> 26:32.632
satiety.

26:32.630 --> 26:37.700
The environment thwarts sensory
specific satiety doesn't it?

26:37.700 --> 26:42.250
Again, if we look back to when
I was a boy, and think about

26:42.252 --> 26:46.182
when you guys were kids,
the number of food choices

26:46.178 --> 26:48.218
available went way up.

26:48.220 --> 26:51.870
There's not only the number of
opportunities to eat has gone

26:51.865 --> 26:55.565
way up because there's food as
we mentioned before in the gas

26:55.574 --> 26:59.534
stations and the drugstores and
schools and everywhere else;

26:59.529 --> 27:02.209
but the variety of foods has
gone up.

27:02.210 --> 27:06.550
Each year the food industry
introduces several thousand new

27:06.551 --> 27:07.751
food products.

27:07.750 --> 27:10.890
Some of them fall out of the
market but some of them don't,

27:10.890 --> 27:13.240
and as a consequence,
the number of options available

27:13.240 --> 27:15.590
today in a supermarket or a
convenience store is way,

27:15.592 --> 27:17.132
way higher then it used to be.

27:17.130 --> 27:20.570
You think of things like this:
a restaurant like the

27:20.573 --> 27:23.273
Cheesecake Factory with an
enormous menu,

27:23.273 --> 27:26.653
or you go to a diner they have
enormous menus.

27:26.650 --> 27:29.560
Anyplace you go it's really--if
you go to a restaurant and they

27:29.559 --> 27:32.329
had like six choices you'd think
you were getting gypped and

27:32.328 --> 27:35.098
there was something wrong with
this place because they don't

27:35.098 --> 27:36.318
have enough choices.

27:36.318 --> 27:38.468
Then of course,
you've got the variety in

27:38.465 --> 27:41.395
places like this,
it's happening around the world

27:41.404 --> 27:44.704
where you have many,
many choices of things in

27:44.703 --> 27:45.813
supermarkets.

27:45.808 --> 27:49.268
In fact, one thing that people
notice when they come from poor

27:49.272 --> 27:50.582
countries to the U.S.

27:50.578 --> 27:52.918
of how many choices we have of
a given food.

27:52.920 --> 27:55.320
So for example,
they often find themselves

27:55.317 --> 27:57.537
perplexed by the number of
cereals,

27:57.538 --> 28:00.288
or the number of different
kinds of coffee,

28:00.288 --> 28:03.638
or how many yogurts there are
in a supermarket.

28:03.640 --> 28:06.680
And it's this dizzying array of
choices whereas in other

28:06.682 --> 28:09.452
countries they have fewer
choices and that actually

28:09.450 --> 28:12.550
would--you would consider them
being helped by that.

28:12.549 --> 28:13.799
Do you have a question?

28:13.798 --> 28:14.448
Student:
>

28:14.450 --> 28:24.500


28:24.500 --> 28:25.530
Prof: Okay,
that's a good question.

28:25.528 --> 28:28.208
The question is with kids
especially does the desire for

28:28.210 --> 28:30.790
variety override the other
sensory properties that you

28:30.794 --> 28:33.284
might get from a high sugar or a
high fat food?

28:33.279 --> 28:36.159
These things are all working in
competition with each other in

28:36.160 --> 28:36.680
some way.

28:36.680 --> 28:41.750
It seems from the bare reality
of the fact that the population

28:41.750 --> 28:44.140
is getting heavier,
and heavier,

28:44.144 --> 28:47.294
and heavier especially kids,
that the things like the fat

28:47.294 --> 28:50.954
and the sugar are overriding the
sensory specific satiety.

28:50.950 --> 28:53.810
Now the sensory specific
satiety would probably have a

28:53.811 --> 28:57.271
chance to kick in if there were
a narrower range of high fat,

28:57.269 --> 29:00.119
high sugar choices,
because then the kid would get

29:00.122 --> 29:02.162
tired of those particular
things;

29:02.160 --> 29:05.080
but there are so many choices
that the sensory specific

29:05.076 --> 29:08.096
satiety probably never really
has a chance to kick in.

29:08.098 --> 29:11.508
You see things like this
happening in all parts of the

29:11.510 --> 29:15.310
world, so of course the food
choices are really dizzying.

29:15.308 --> 29:18.348
Now it's interesting how
Frito-Lay marketed this

29:18.351 --> 29:19.711
particular product.

29:19.710 --> 29:24.740
First, on the package it says
eight vitamins--fortified with

29:24.743 --> 29:28.673
eight vitamins and
minerals--essential ones.

29:28.670 --> 29:31.740
Now what do you guys think
about that?

29:31.740 --> 29:36.150
Is this a reasonable thing for
a company to do?

29:36.150 --> 29:38.690
Okay why?

29:38.690 --> 29:42.140
Student:
>

29:42.140 --> 29:44.650
Prof: Okay,
so one vote would be yes this

29:44.651 --> 29:47.271
is okay because vitamins and
minerals are good.

29:47.269 --> 29:49.729
If kids are going to eat it
anyway, why not throw some of

29:49.730 --> 29:50.170
them in?

29:50.170 --> 29:51.940
Okay what about some other
reactions to this?

29:51.940 --> 29:53.820
Yes.

29:53.818 --> 29:54.458
Student:
>

29:54.460 --> 29:58.590


29:58.588 --> 30:00.698
Prof: Okay,
maybe the body's not going to

30:00.700 --> 30:02.900
use these vitamins anyway so
what use are they?

30:02.900 --> 30:03.660
Yes.

30:03.660 --> 30:07.250
Student:
>

30:07.250 --> 30:08.420
Prof: Yes,
I'll talk about that in just a

30:08.415 --> 30:08.635
minute?

30:08.640 --> 30:10.160
Student:
>

30:10.160 --> 30:10.910
Prof: Yes.

30:10.910 --> 30:11.480
Student:
>

30:11.480 --> 30:15.870


30:15.868 --> 30:19.198
Prof: Okay so the--this
comment was that this is

30:19.203 --> 30:22.973
probably not good because there
is more harm--I'm assuming you

30:22.971 --> 30:25.751
mean from eating the high
calorie foods,

30:25.750 --> 30:28.170
then you get the good from
coming from the vitamins and

30:28.174 --> 30:28.674
minerals.

30:28.670 --> 30:30.550
This is an interesting thing.

30:30.548 --> 30:32.308
Now food companies do this a
lot.

30:32.308 --> 30:35.008
They fortify foods,
and there are cases where the

30:35.009 --> 30:37.089
fortification is a very good
thing.

30:37.088 --> 30:39.958
For example,
we mentioned before,

30:39.961 --> 30:45.081
putting iodine in salt helps
prevent certain deficiencies,

30:45.075 --> 30:50.365
folic acid and certain things
helps birth deficiencies;

30:50.368 --> 30:53.168
so there's some examples of
where fortification is a good

30:53.173 --> 30:56.133
public health move and there's
general consensus that it's a

30:56.127 --> 30:56.827
good idea.

30:56.828 --> 30:59.598
In this case,
the vitamins and minerals don't

30:59.598 --> 31:03.438
need to be in there because (a)
American children for the most

31:03.438 --> 31:06.458
part are not vitamin and mineral
deficient,

31:06.460 --> 31:09.940
and (b) if you want to deliver
more vitamins and minerals to

31:09.942 --> 31:13.902
American children this would not
be the vehicle you would choose.

31:13.900 --> 31:20.590
Some critics of these kind of
practices say that this is just

31:20.587 --> 31:23.297
pure,
base marketing to make what

31:23.298 --> 31:27.008
otherwise is not a very good
food seem healthier then it

31:27.006 --> 31:29.376
really is,
and make it--it gives

31:29.383 --> 31:31.843
permission for parents to buy
this.

31:31.838 --> 31:33.778
It says yes,
this is a good thing to buy

31:33.779 --> 31:35.919
because it's going to help your
children.

31:35.920 --> 31:37.680
That's what the critics would
say.

31:37.680 --> 31:41.600
Now over on the right hand side
up there you see that there's

31:41.596 --> 31:43.226
more permission giving.

31:43.230 --> 31:47.120
It says that kids will love
this--sorry this is cut off,

31:47.124 --> 31:49.884
but it said,
this is something that both

31:49.884 --> 31:52.014
kids and mothers can love.

31:52.009 --> 31:55.759
It gives mothers permission to
buy it and it has all their

31:55.760 --> 31:59.870
favorite snacks in one bag;
and then down here you see that

31:59.868 --> 32:04.128
it's eight vitamins and minerals
again, no Trans fat and it's

32:04.125 --> 32:06.675
endorsed by Dr. Kenneth
Cooper.

32:06.680 --> 32:10.010
Cooper is the father of
aerobics.

32:10.009 --> 32:12.969
He's a physician who has a
large practice in Dallas,

32:12.970 --> 32:17.130
a place called the Cooper
Center and he was really the--he

32:17.125 --> 32:20.395
was a cardiologist,
still is a cardiologist,

32:20.397 --> 32:23.457
but he invented the concept of
aerobics many,

32:23.458 --> 32:24.708
many years ago.

32:24.710 --> 32:26.630
He's gotten in on the nutrition
field as well.

32:26.630 --> 32:30.610
Well, he started working with
PepsiCo which is the parent

32:30.613 --> 32:32.253
company to Frito-Lay.

32:32.250 --> 32:35.280
In fact, we'll have a terrific
person from PepsiCo as one of

32:35.284 --> 32:38.524
our guest speakers later in the
class and he can talk about this

32:38.523 --> 32:39.453
sort of thing.

32:39.450 --> 32:41.840
Cooper then gets paid as a
consultant,

32:41.838 --> 32:44.038
I don't know how much,
but he gets paid as a

32:44.044 --> 32:46.864
consultant and then issues his
proclamation that this is

32:46.863 --> 32:48.353
somehow a healthier food.

32:48.348 --> 32:51.498
But if, in fact,
kids are eating more of it,

32:51.500 --> 32:53.860
and getting more calories,
more fat,

32:53.858 --> 32:56.388
and more sugar then they would
if they were just eating the

32:56.387 --> 32:58.747
regular old food,
then this does seem like

32:58.748 --> 33:00.608
marketing that's ill-advised.

33:00.608 --> 33:04.048
Putting out a product and then
marketing it as somehow healthy

33:04.053 --> 33:06.653
when kids might eat more of it
is a problem.

33:06.650 --> 33:09.840
Okay, well we did a little
study on this that we haven't

33:09.843 --> 33:12.363
yet published,
but we did a study where we

33:12.355 --> 33:15.585
brought kids into the lab,
we found out which of these

33:15.590 --> 33:19.200
foods they liked the most and
then gave them that versus the

33:19.198 --> 33:22.438
Munchies and--to see which they
would eat more of.

33:22.440 --> 33:25.030
You guys were exactly right:
they didn't eat less of the

33:25.030 --> 33:27.670
Munchies, they didn't eat the
same, but they ate more.

33:27.670 --> 33:33.150
This creates interesting issues
with marketing and biology and

33:33.147 --> 33:36.917
the whole sensory specific
satiety idea.

33:36.920 --> 33:40.940
Now I'd like turn our attention
to the effect of things like

33:40.942 --> 33:44.422
stress and a related concept on
social position,

33:44.420 --> 33:45.730
because a study came out
recently on this,

33:45.734 --> 33:46.574
it was very interesting.

33:46.568 --> 33:50.858
There's been a lot of interest
in the influence of stress on

33:50.855 --> 33:54.845
eating and stress affects
eating, as I mentioned before,

33:54.851 --> 33:56.451
in different ways.

33:56.450 --> 33:58.350
Let's just get a show of hands
on this,

33:58.348 --> 34:01.628
if you're stressed--now most
people when they're severely

34:01.625 --> 34:05.305
stressed like things are really
just out of control in life tend

34:05.310 --> 34:07.710
to eat less and eating is
suppressed.

34:07.710 --> 34:09.950
But at more moderate levels of
stress,

34:09.949 --> 34:12.449
the kind of things that you'd
experience are studying for a

34:12.447 --> 34:15.417
tough exam,
you got relationship issues

34:15.422 --> 34:17.732
going on,
family things happening or

34:17.731 --> 34:20.471
whatever it happens to be--How
many of you under those

34:20.469 --> 34:22.379
conditions would say you eat
more?

34:22.380 --> 34:25.270
How many of you would say you
eat less?

34:25.268 --> 34:28.888
Okay, so an interesting mix
within this kind of class.

34:28.889 --> 34:32.699
It's interesting that the
preponderant response here was

34:32.699 --> 34:33.669
to eat more.

34:33.670 --> 34:35.930
There's biology that affects
that.

34:35.929 --> 34:39.499
Well there was a graduate
student working with me several

34:39.501 --> 34:42.691
years--a number of years ago
named Elissa Epel,

34:42.690 --> 34:45.450
here's a picture of Elissa
who's now on the faculty at The

34:45.452 --> 34:47.442
University of California San
Francisco.

34:47.440 --> 34:50.440
She looks like she's on TV at
this point because that's a

34:50.438 --> 34:51.668
picture of her on TV.

34:51.670 --> 34:55.970
Anyway, Elissa was a terrific,
terrific young scholar working

34:55.972 --> 35:00.282
with us and now has become the
leading expert in the world on

35:00.275 --> 35:02.135
this particular topic.

35:02.139 --> 35:05.289
She got interested in why
stress makes some people eat

35:05.288 --> 35:07.188
more and some people eat less.

35:07.190 --> 35:10.250
Depression does the same thing
by the way, and there were a

35:10.251 --> 35:13.051
number of interesting reasons
why this might occur.

35:13.050 --> 35:15.800
What does stress actually do
that would affect somebody's

35:15.802 --> 35:16.492
food intake?

35:16.489 --> 35:21.369
Well, it could stimulate hunger
so people just want to eat more.

35:21.369 --> 35:24.899
It could stimulate satiety or
suppress satiety,

35:24.898 --> 35:29.118
either one depending on whether
you eat more or less.

35:29.119 --> 35:32.009
If could affect the
sensory--how you perceive food,

35:32.010 --> 35:36.270
so if you get a certain amount
of pleasure from food x,

35:36.268 --> 35:39.208
pizza let's say,
or a donut, or ice cream or

35:39.210 --> 35:43.110
whatever it happens to be under
non-stress conditions,

35:43.110 --> 35:46.330
the one hypothesis is that if
you're stressed your physiology

35:46.329 --> 35:49.279
changes so you derive more
pleasure from those foods.

35:49.280 --> 35:53.050
It could be that the foods have
a whole different biological

35:53.052 --> 35:55.422
effect then what I just
mentioned,

35:55.420 --> 35:59.190
and that would the ability to
calm down this arousal that gets

35:59.186 --> 36:01.406
produced in people by feeling
bad.

36:01.409 --> 36:04.909
When you're stressed,
your body is aroused.

36:04.909 --> 36:07.609
You've all heard of the fight
or flight thing,

36:07.610 --> 36:10.140
your body is aroused,
that becomes an unpleasant

36:10.141 --> 36:13.371
state for most people,
and one hypothesis is you eat

36:13.369 --> 36:15.899
the food and it calms that state
down.

36:15.900 --> 36:18.000
It soothes you down and makes
you feel better.

36:18.000 --> 36:21.720
Elissa did a variety of studies
to test these different

36:21.722 --> 36:22.622
hypothesis.

36:22.619 --> 36:25.259
We're not going to talk too
much about the results now

36:25.260 --> 36:28.100
because there--they would take a
long time to discuss,

36:28.099 --> 36:30.689
but they're extremely
interesting and she found not

36:30.688 --> 36:33.948
only does stress affect biology
through some of these mechanisms

36:33.952 --> 36:37.202
that we talked about,
but also it affects things like

36:37.195 --> 36:40.435
what the body does with the
nutrients that come in,

36:40.440 --> 36:43.850
and when they get stored on the
body as body fat where they

36:43.847 --> 36:44.727
happen to go.

36:44.730 --> 36:48.470
There is--she has research
showing that when eating is done

36:48.472 --> 36:51.962
under stress conditions and
there's a Cortisol response

36:51.958 --> 36:55.378
that's related to stress--you'll
learn more about that

36:55.380 --> 36:58.930
later--that the body is more
likely to take that fat and

36:58.929 --> 37:02.999
store it in the abdominal region
and we'll come back and mention

37:02.996 --> 37:05.446
that again as we did before.

37:05.449 --> 37:09.179
A very interesting study
related to this was published

37:09.181 --> 37:13.331
just a short time ago using
monkeys at a well known research

37:13.333 --> 37:16.013
facility in Atlanta called
Yerkes.

37:16.010 --> 37:19.790
What they wanted-- what these
researchers wanted to do is look

37:19.786 --> 37:22.756
at social position within a
group of monkeys,

37:22.760 --> 37:26.450
looking at the dominant monkeys
in social groups versus the

37:26.449 --> 37:27.849
subordinate monkeys.

37:27.849 --> 37:30.379
If--those of you who have had
any psychology will have heard

37:30.380 --> 37:31.540
about this kind of thing.

37:31.539 --> 37:37.269
This is not an infrequently
used measure of stress,

37:37.268 --> 37:40.908
and how social position affects
stress and therefore affects

37:40.909 --> 37:43.439
whatever outcome you're
interested in,

37:43.440 --> 37:45.800
in this case,
they were interested in eating.

37:45.800 --> 37:49.300
What they found here is that
they compared females in this

37:49.304 --> 37:53.064
particular study and looked at
the dominant versus subordinate

37:53.056 --> 37:53.606
ones.

37:53.610 --> 37:56.270
Both groups could eat all they
wanted whenever they wanted

37:56.268 --> 37:58.698
because food was freely
available, that's what the ad

37:58.695 --> 37:59.345
lib means.

37:59.349 --> 38:02.189
The subordinates ate less,
but not a lot less,

38:02.190 --> 38:05.440
than the dominant monkeys,
and the researchers

38:05.443 --> 38:09.573
hypothesized that this might be
due to the stress of being

38:09.565 --> 38:13.955
subordinate in the social group;
but they were more or less

38:13.956 --> 38:17.736
maintaining a normal weight and
able to function fine.

38:17.739 --> 38:21.259
They then introduced high fat,
high calorie foods to these

38:21.260 --> 38:24.000
animals,
and allowed both groups to eat

38:24.001 --> 38:27.231
ad lib: whatever they wanted of
the high fat,

38:27.230 --> 38:29.540
high calorie foods and
something very interesting

38:29.536 --> 38:30.736
happened at that point.

38:30.739 --> 38:35.159
The dominants ate a bit more of
this food but not an awful lot,

38:35.159 --> 38:37.729
but the subordinates ate much
more,

38:37.730 --> 38:41.330
especially at night when
they're not ordinarily eating.

38:41.329 --> 38:44.849
There were--the researcher--and
of course gained weight as a

38:44.851 --> 38:48.311
consequence and the researchers
hypothesized that maybe the

38:48.313 --> 38:52.143
calorie dense foods became a way
to soothe the stress to calm the

38:52.135 --> 38:55.115
arousal and had much more
reinforcing value for the

38:55.119 --> 38:58.109
subordinate animals because of
the stress they were

38:58.105 --> 39:01.205
experiencing due to their social
position.

39:01.210 --> 39:05.530
Very interesting study here;
so here you get a different

39:05.534 --> 39:10.824
effect of introduction of a high
fat diet depending on where the

39:10.818 --> 39:14.088
organism is in the social
position,

39:14.090 --> 39:18.760
another interesting phenomenon
that has to do with stress.

39:18.760 --> 39:22.590
Here would be a perfect example
of how environment affects

39:22.586 --> 39:25.336
biology, which in turn,
affects eating.

39:25.340 --> 39:30.630
Let's talk a little bit about
body composition.

39:30.630 --> 39:34.340
What does the body do with
calories and how does body

39:34.344 --> 39:36.564
weight get affected by these?

39:36.559 --> 39:39.349
Again, you're going to start to
see both culture,

39:39.349 --> 39:42.599
which we'll talk about later,
but you talk about things

39:42.601 --> 39:46.341
external to the body and things
internal to the body having big

39:46.336 --> 39:47.056
impacts.

39:47.059 --> 39:50.339
Biology is a player certainly
in how people regulate their

39:50.335 --> 39:51.135
body weight.

39:51.139 --> 39:55.259
The fact is,
if we took all of you and put

39:55.255 --> 40:00.735
you on exactly the same diet,
the precise number of calories,

40:00.737 --> 40:03.517
the precise composition of the
food,

40:03.518 --> 40:06.508
x carbohydrate,
x fat, those sort of things and

40:06.507 --> 40:09.427
you all exercised exactly the
same amount,

40:09.429 --> 40:12.149
and we did this under very
controlled circumstances there

40:12.152 --> 40:14.932
would be large variations and
what your body would do with

40:14.925 --> 40:15.845
those calories.

40:15.849 --> 40:18.589
On a given calorie level,
some of you might lose weight

40:18.590 --> 40:20.470
and some of you might gain
weight,

40:20.469 --> 40:23.749
some of you would stay the
same, and if you plotted--did a

40:23.751 --> 40:26.801
scatter plot of what would
happen to your weight under

40:26.802 --> 40:29.932
identical conditions,
there would be pretty large

40:29.932 --> 40:30.722
variability.

40:30.719 --> 40:33.589
That's all the biology that
you're born with.

40:33.590 --> 40:38.110
Your biology determines to a
great extent how your body

40:38.114 --> 40:41.634
handles calories,
how efficiently it stores them,

40:41.626 --> 40:45.346
how it burns them off,
how it wastes them through body

40:45.349 --> 40:47.419
heat,
what it does with your basal

40:47.422 --> 40:50.312
metabolic rate that I'll
describe in a moment and the

40:50.306 --> 40:50.746
like.

40:50.750 --> 40:53.610
This has been shown a number of
ways.

40:53.610 --> 40:56.630
Weight is driven,
as you know,

40:56.634 --> 40:58.724
by energy balance.

40:58.719 --> 41:00.559
It's calories in and calories
out.

41:00.559 --> 41:04.129
Calories as we mentioned before
are just a measure of energy,

41:04.130 --> 41:05.380
the unit of energy.

41:05.380 --> 41:10.210
If energy intake is different
then the energy expenditure the

41:10.208 --> 41:14.878
difference is felt more or
less--most often in the body fat

41:14.876 --> 41:19.136
stores and that determines
whether people lose or gain

41:19.143 --> 41:21.803
weight,
and as you can imagine,

41:21.802 --> 41:24.932
energy balance is affected in
the following way:

41:24.934 --> 41:28.804
if intake is less then
expenditure weight loss occurs,

41:28.800 --> 41:32.140
so this is of course where
hunger and starvation come in.

41:32.139 --> 41:35.739
Weight gain occurs under these
conditions and you have stable

41:35.739 --> 41:38.319
weight if intake and expenditure
line up.

41:38.320 --> 41:43.060
Now you have problems with the
top two features here in

41:43.059 --> 41:47.419
different parts of the world,
overnutrition in some parts,

41:47.422 --> 41:50.672
undernutrition in others,
and it's the energy balance

41:50.672 --> 41:53.272
that's helping drive the weight
itself.

41:53.268 --> 41:56.548
Let's talk about metabolism for
a minute.

41:56.550 --> 41:59.480
The word metabolism comes up a
lot.

41:59.480 --> 42:02.940
There are metabolism diets;
people say I have fast

42:02.942 --> 42:06.862
metabolism, you have slow
metabolism, people use that word

42:06.860 --> 42:07.480
a lot.

42:07.480 --> 42:08.560
What does it mean?

42:08.559 --> 42:11.859
Well metabolism is essentially
the term that describes how your

42:11.858 --> 42:13.028
body handles energy.

42:13.030 --> 42:17.280
When you consume food,
metabolic processes occur that

42:17.278 --> 42:22.258
then--that are pertaining to the
way your body is handling the

42:22.262 --> 42:25.392
incoming energy,
the incoming nutrients,

42:25.387 --> 42:28.137
and all of this is our
metabolic processes and

42:28.141 --> 42:30.651
metabolism is the end product of
that.

42:30.650 --> 42:36.090
This would be--this particular
chart here shows calories people

42:36.094 --> 42:40.844
burn, the energy expenditure
part of the energy balance

42:40.838 --> 42:41.978
equation.

42:41.980 --> 42:46.050
How many calories are you
burning just sitting there?

42:46.050 --> 42:49.410
If you stand up would you burn
more calories?

42:49.409 --> 42:52.059
If you walked across the room
would you burn more?

42:52.059 --> 42:54.549
If you ran a marathon how many
would you burn?

42:54.550 --> 42:58.850
How many calories is your body
burning just by virtue of being

42:58.846 --> 42:59.406
alive?

42:59.409 --> 43:02.229
These are all parts of the
energy balance equation,

43:02.226 --> 43:03.856
part of energy expenditure.

43:03.860 --> 43:06.920
Let's look at the left hand
side of this bar and we're going

43:06.920 --> 43:08.530
to see how it all partitions.

43:08.530 --> 43:12.560
What this particular bar shows
is that this might be typical

43:12.559 --> 43:15.289
energy expenditure for an
average person,

43:15.293 --> 43:17.483
so say 2,000 calories a day.

43:17.480 --> 43:20.420
Now by the way,
that's why when you see the

43:20.416 --> 43:23.976
dietary recommendations,
that the average person eat

43:23.981 --> 43:26.151
about 2,000 calories a day.

43:26.150 --> 43:28.770
It would be in balance:
you're burning as much as you

43:28.771 --> 43:30.741
eat, you're not going to gain
weight.

43:30.739 --> 43:35.199
The pieces of this,
first BMR, basal metabolic

43:35.195 --> 43:35.885
rate.

43:35.889 --> 43:39.659
That's how many calories--how
much energy your body is using

43:39.655 --> 43:40.865
just to function.

43:40.869 --> 43:44.349
So your cells are dividing,
your heart is beating,

43:44.349 --> 43:47.169
your blood is pumping,
there's some movement that

43:47.168 --> 43:50.808
occurs even if you're not--if
you're being pretty sedentary.

43:50.809 --> 43:56.569
It's just the body's need to
function, is the basal metabolic

43:56.568 --> 43:57.238
rate.

43:57.239 --> 44:04.419
You can see that of the 2,000
calories, that constitutes a lot

44:04.418 --> 44:05.358
of it.

44:05.360 --> 44:08.530
Now the sleeping metabolic
rate, which is less then the

44:08.530 --> 44:11.230
basal metabolic rate,
is how much energy you're

44:11.231 --> 44:12.701
burning during sleep.

44:12.699 --> 44:15.999
You'd be burning less during
sleep because of course you're

44:16.001 --> 44:19.591
not being as active and the body
doesn't need as much energy.

44:19.590 --> 44:23.450
This TEF is called the thermic
effect of food.

44:23.449 --> 44:27.189
When you eat a meal or you eat
any food your body takes in the

44:27.188 --> 44:31.248
calories and makes use of it,
but it requires bodily

44:31.246 --> 44:35.026
processes to do that,
and those bodily processes

44:35.025 --> 44:37.585
require energy for your stomach
to work,

44:37.590 --> 44:39.320
your intestines to work and the
like.

44:39.320 --> 44:42.180
That accounts for the little
bit of the energy expenditure

44:42.177 --> 44:42.977
but not a lot.

44:42.980 --> 44:47.760
The SPA is spontaneous physical
activity.

44:47.760 --> 44:52.750
You can see from this graph
that not an awful lot of the

44:52.746 --> 44:57.276
calories that people burn
overall are driven by how

44:57.279 --> 45:00.089
physically active they are.

45:00.090 --> 45:03.760
Now being physically active is
terribly important with

45:03.764 --> 45:06.404
regulation of the balance of
energy.

45:06.400 --> 45:09.660
So it's really important,
but as important or even much

45:09.655 --> 45:12.655
more important,
is the biology people are born

45:12.659 --> 45:16.369
with that affects things like
their basal metabolic rate.

45:16.369 --> 45:19.489
That's why all of you on
exactly the same diet,

45:19.489 --> 45:22.459
exactly the same exercise plan
would not have the same thing

45:22.456 --> 45:25.366
happen to your weights because
of the differences in things

45:25.371 --> 45:26.881
like basal metabolic rate.

45:26.880 --> 45:29.340
Some people are very efficient
with their calories,

45:29.340 --> 45:32.690
as we mentioned before,
they consume calories-a certain

45:32.693 --> 45:35.553
amount of calories,
the body doesn't waste them

45:35.554 --> 45:38.494
through heat and by unnecessary
metabolic processes;

45:38.489 --> 45:42.199
they store those calories in
the body fat when it becomes

45:42.199 --> 45:43.989
more then the body needs.

45:43.989 --> 45:47.399
Other people are less efficient
with calories,

45:47.402 --> 45:51.732
they're consuming calories but
their body is wasting them,

45:51.726 --> 45:52.936
if you will.

45:52.940 --> 45:56.540
Now wasting the calories
through high-level metabolic

45:56.541 --> 46:00.351
processes is a good thing in a
culture of abundance,

46:00.349 --> 46:04.209
but it's a bad thing if
starvation is a factor in one's

46:04.208 --> 46:04.708
life.

46:04.710 --> 46:07.040
Then you want to be as
efficient with the calories as

46:07.036 --> 46:07.526
possible.

46:07.530 --> 46:12.410
Overall, the amount of physical
activity people get as a

46:12.411 --> 46:17.301
function of overall energy
expenditure is not as much as

46:17.295 --> 46:19.795
you might think,
but important,

46:19.797 --> 46:22.027
very important,
I don't want to underplay that,

46:22.030 --> 46:24.970
but these other factors are
very important as well in

46:24.969 --> 46:26.779
determining what people weigh.

46:26.780 --> 46:30.110
If we look at energy
expenditure components they

46:30.108 --> 46:34.358
change depending on whether a
person is physically active.

46:34.360 --> 46:37.980
We look at the three major
things: the thermic effect of

46:37.983 --> 46:41.373
food which is a small part,
the energy expenditure from

46:41.369 --> 46:43.449
physical energy,
and then resting energy

46:43.451 --> 46:46.381
expenditure which is essentially
the basal metabolic rate.

46:46.380 --> 46:51.140
For a sedentary person that
might burn 1,800 calories a day,

46:51.139 --> 46:53.399
the partitioning will look like
this,

46:53.400 --> 46:57.010
but for somebody who's more
physically active and might burn

46:57.007 --> 47:00.677
400 more calories a day,
say by walking four miles,

47:00.679 --> 47:03.509
running four miles,
or doing other things,

47:03.507 --> 47:06.067
the partitioning would look
like this and so the

47:06.068 --> 47:08.838
expenditure,
the orange part of the circles

47:08.836 --> 47:11.616
here from physical activity goes
up a lot,

47:11.619 --> 47:15.409
by 400 calories to be precise,
but it also becomes a greater

47:15.413 --> 47:18.953
percentage of the overall energy
expenditure picture.

47:18.949 --> 47:21.459
That's why being physically
active is such a good idea.

47:21.460 --> 47:26.740
We'll talk about body
composition as well.

47:26.739 --> 47:30.809
Weight is one index of how
healthy people are but also how

47:30.809 --> 47:33.949
much body fat they have is
important as well,

47:33.951 --> 47:35.381
and that varies.

47:35.380 --> 47:38.680
You can have people at a given
weight who vary a lot in their

47:38.675 --> 47:39.825
amount of body fat.

47:39.829 --> 47:43.439
It's the relative amount of fat
and lean body tissue,

47:43.440 --> 47:46.180
lean body tissue is the
combination of muscle,

47:46.179 --> 47:48.759
bones and organs,
but people usually refer to

47:48.755 --> 47:52.435
lean body mass as muscle because
that's the most important thing

47:52.442 --> 47:57.022
that you have some control over;
and then that compares to body

47:57.016 --> 47:59.756
fat,
so percent body fat of course

47:59.757 --> 48:04.247
would be a person weighs 100
pounds and if 30% of it is fat

48:04.253 --> 48:07.203
then their percent body fat is
30%.

48:07.199 --> 48:10.139
There are different ways of
measuring body fat.

48:10.139 --> 48:13.939
Three of the primary ones are
by looking at body fat skin

48:13.938 --> 48:17.938
folds, by weighing people under
water, and by using a device

48:17.940 --> 48:19.840
called the Dexa machine.

48:19.840 --> 48:24.140
Here's what they look like,
the Dexa machine on the bottom,

48:24.139 --> 48:27.209
the top left is measuring body
fat with skin folds,

48:27.210 --> 48:30.040
through skin folds,
and there is a device called

48:30.041 --> 48:33.661
skin fold calipers that you see
a researcher there using on a

48:33.655 --> 48:37.385
part of the back there where a
pinch of body fat is made,

48:37.389 --> 48:40.439
the thickness of it is measured
with the calipers.

48:40.440 --> 48:42.680
That's done on several places
in the body,

48:42.679 --> 48:45.529
and then you can use equations
to figure out from that,

48:45.530 --> 48:48.890
estimate from that how much
body fat a person actually has.

48:48.889 --> 48:52.139
The most precise way,
the gold standard of measuring

48:52.141 --> 48:55.971
this, is to weigh people under
water which is what you see in

48:55.967 --> 48:57.177
the upper left.

48:57.179 --> 49:00.199
A person sits on a chair,
is submerged in water for just

49:00.204 --> 49:03.024
a very short time,
and their body fat can get

49:03.021 --> 49:06.731
estimated from that because lean
tissue and fat tissue have

49:06.730 --> 49:10.120
different densities and that can
be determined through

49:10.119 --> 49:11.269
calculations.

49:11.268 --> 49:14.658
Of course you could imagine how
this is difficult to do with

49:14.659 --> 49:17.879
large numbers of people and if
you have to have the right

49:17.875 --> 49:18.675
equipment.

49:18.679 --> 49:20.499
The same would be true of the
Dexa machine,

49:20.500 --> 49:23.810
which provides a pretty good
estimate of body fat as well,

49:23.809 --> 49:27.229
but then requires lots of money
and things like that.

49:27.230 --> 49:31.390
Even doing skin fold calipers,
which doesn't require the

49:31.389 --> 49:34.549
expensive equipment,
it requires time and training,

49:34.554 --> 49:36.934
and so if you're doing a large
population study,

49:36.929 --> 49:38.469
say with thousands and
thousands of people,

49:38.472 --> 49:39.392
that becomes a barrier.

49:39.389 --> 49:43.779
So you can see why most studies
just measure body weight in

49:43.777 --> 49:48.157
people, even if they're just
reporting it themselves rather

49:48.164 --> 49:50.514
then at measuring body fat.

49:50.510 --> 49:54.240
Then those measures get put
into this thing called the body

49:54.242 --> 49:55.082
mass index.

49:55.079 --> 49:57.599
Let's talk now about what that
is.

49:57.599 --> 50:00.969
There's been the need to
develop an approximation of body

50:00.974 --> 50:04.454
fat because body fat,
percent body fat is a stronger

50:04.445 --> 50:07.275
predictor of health then body
weight is,

50:07.280 --> 50:11.090
because with body weight you
get false positives and false

50:11.094 --> 50:13.844
negatives as I'll describe in a
moment.

50:13.840 --> 50:16.570
You can't measure body fat all
the time because it costs too

50:16.570 --> 50:17.820
much, it's too difficult.

50:17.820 --> 50:21.530
So the body mass index was
developed.

50:21.530 --> 50:26.430
What it is, it's an equation
that takes weight and height and

50:26.432 --> 50:31.502
puts them together into a number
that is--then becomes a health

50:31.498 --> 50:36.238
standard and the number is an
estimate of how much body fat

50:36.239 --> 50:37.709
people have.

50:37.710 --> 50:41.310
It's not an estimate of percent
body fat but it better reflects

50:41.306 --> 50:44.956
body fat then if all you know
about somebody is their weight.

50:44.960 --> 50:47.400
If you get somebody's height
and their weight,

50:47.400 --> 50:49.930
you create the body mass index,
which is weight,

50:49.929 --> 50:54.379
in this case expressed in
kilograms divided by height

50:54.378 --> 50:58.168
squared,
expressed as meters and then

50:58.170 --> 51:03.870
you come away with numbers that
usually range from 12,

51:03.869 --> 51:07.299
13 at the very low end up into
the 60s or even higher at the

51:07.298 --> 51:08.228
very high end.

51:08.230 --> 51:13.110
In this particular graphic you
see what the classifications are

51:13.110 --> 51:15.470
for underweight,
normal weight,

51:15.471 --> 51:18.621
and overweight with body mass
index.

51:18.619 --> 51:21.509
Now you guys have probably
heard of this concept,

51:21.510 --> 51:24.830
you've seen charts with it,
you may have calculated your

51:24.827 --> 51:28.567
own body mass index or--plenty
of places you can do that on the

51:28.568 --> 51:32.488
web--and you can get a sense
therefore about what this means.

51:32.489 --> 51:35.219
This is a better measure then
just body fat.

51:35.219 --> 51:38.869
Now body fat and body
composition are usually alike.

51:38.869 --> 51:40.109
But not always.

51:40.110 --> 51:44.180
That's where you get the false
positives and false negatives.

51:44.179 --> 51:46.879
For example,
you can get exception like this

51:46.882 --> 51:48.832
football player,
for example.

51:48.829 --> 51:52.209
If all you knew was his weight
you would assume the guy weighs

51:52.213 --> 51:54.563
too much,
but he's very heavily muscled

51:54.556 --> 51:57.156
so that would be a false
positive for obesity,

51:57.157 --> 51:57.907
let's say.

51:57.909 --> 52:03.419
You can have cases where weight
can be good but body composition

52:03.423 --> 52:05.643
is bad,
so that would mean somebody is

52:05.637 --> 52:07.817
actually normal weight but
they're in bad physical

52:07.824 --> 52:09.814
condition,
they don't have much muscle,

52:09.811 --> 52:12.051
and they have more fat then
would be optimal.

52:12.050 --> 52:14.630
Of course you can have the
opposite where weight could

52:14.628 --> 52:17.448
appear to be bad but body
composition is actually good,

52:17.449 --> 52:20.509
so that's why you need
something that is an estimate of

52:20.514 --> 52:21.144
body fat.

52:21.139 --> 52:25.979
Here was a graph that shows the
relationship between body mass

52:25.978 --> 52:30.028
index and actual body fat
measured by something like

52:30.025 --> 52:31.925
underwater weighing.

52:31.929 --> 52:34.429
You get a large sample of
people, in this particular

52:34.429 --> 52:36.989
study,
each dot represents a subject

52:36.989 --> 52:40.279
in here and you take their body
mass index,

52:40.280 --> 52:43.430
just by knowing their height
and weight and you see how

52:43.434 --> 52:45.484
carefully it represents body
fat.

52:45.480 --> 52:49.160
The women are in the lighter
colored, the orange colored

52:49.164 --> 52:52.654
squares and the men are in the
blue colored ovals.

52:52.650 --> 52:57.490
The lines here show
that--several things.

52:57.489 --> 53:00.489
This is the average for the
women, the line that goes right

53:00.490 --> 53:02.770
through there;
this would be the average for

53:02.768 --> 53:04.418
the man that goes through there.

53:04.420 --> 53:07.370
What you see is a pretty nice
relationship that,

53:07.369 --> 53:12.259
as body mass index increases
body fat increases as well,

53:12.260 --> 53:14.970
and if this were a perfect
predictor,

53:14.969 --> 53:17.799
if body mass index were a
perfect estimator of body fat

53:17.798 --> 53:20.888
you'd see an exactly straight
line that would go at an angle

53:20.889 --> 53:23.299
right like this,
but it would be straight.

53:23.300 --> 53:26.940
So these lines are almost
straight but there is some curve

53:26.940 --> 53:27.580
in them.

53:27.579 --> 53:30.189
So it's not perfect but it's
pretty good.

53:30.190 --> 53:33.150
You can see here that women
have more body fat then men,

53:33.148 --> 53:35.838
that's a natural thing,
and I'll describe that in a

53:35.838 --> 53:38.468
minute;
and also there is some

53:38.465 --> 53:41.145
variability in these things.

53:41.150 --> 53:44.030
Let's just take a couple of
data points as an example.

53:44.030 --> 53:45.670
Let's see.

53:45.670 --> 53:49.530
Let's look at the squares for
women that you see in the

53:49.525 --> 53:50.235
circles.

53:50.239 --> 53:53.819
Here would be a case of
individual subjects in this

53:53.820 --> 53:57.540
study who have the same body
mass index but have much

53:57.543 --> 53:59.193
different body fat.

53:59.190 --> 54:02.670
This shows that there's some
error in this,

54:02.670 --> 54:05.030
that's it's not a perfect
measure of body fat,

54:05.030 --> 54:09.380
because two women who have body
mass index of about 20 which is

54:09.382 --> 54:13.532
in the healthy range have as
little as about 12% body fat or

54:13.525 --> 54:14.855
as much as 40%.

54:14.860 --> 54:17.390
This would be examples of
somebody who's weights are about

54:17.394 --> 54:19.984
the same at a given height but
they're in different kind of

54:19.976 --> 54:22.286
physical condition,
so there's some error there.

54:22.289 --> 54:25.879
There's also error in this
direction where you'd have two

54:25.876 --> 54:28.626
people with the same amount of
body fat,

54:28.630 --> 54:31.160
but would have a different body
mass index,

54:31.159 --> 54:33.329
and they're quite different
from one another.

54:33.329 --> 54:36.579
There are different errors in
this but for the most part body

54:36.579 --> 54:39.289
mass index is about the best
predictor we have,

54:39.289 --> 54:43.219
short of doing physiological
assessment of what body fat

54:43.224 --> 54:44.804
really is in people.

54:44.800 --> 54:47.980
A few composition--facts about
body composition,

54:47.978 --> 54:51.088
as I said, women
have--naturally have more body

54:51.090 --> 54:52.240
fat then men.

54:52.239 --> 54:55.829
That is probably due to the
need to preserve energy for

54:55.833 --> 54:59.233
pregnancy and lactation,
I mentioned that before.

54:59.230 --> 55:02.700
Strength training increases
muscle mass in the body and that

55:02.699 --> 55:06.169
changes body composition even if
the amount of fat stays the

55:06.170 --> 55:06.700
same.

55:06.699 --> 55:09.979
Muscle is more metabolically
active then fat.

55:09.980 --> 55:13.170
It takes more energy for the
body to deal with a pound of

55:13.172 --> 55:15.912
muscle then it does with a pound
of body fat,

55:15.909 --> 55:18.859
so that's why things like
strength training become a very

55:18.864 --> 55:22.034
good idea if one's concerned
about body weight regulation.

55:22.030 --> 55:25.010
It's good for lots of different
reasons but that would be one of

55:25.009 --> 55:25.339
them.

55:25.340 --> 55:30.910
Normal body fat is about 15% to
17% of body weight for men,

55:30.911 --> 55:36.581
20% to 25% body fat for women
would be the typical range.

55:36.579 --> 55:39.169
As I mentioned before,
men and women tend to store

55:39.170 --> 55:42.550
weight in different parts of the
body, and you have the apple and

55:42.554 --> 55:43.564
the pear shape.

55:43.559 --> 55:47.399
I also mentioned before,
there are exceptions to this

55:47.402 --> 55:47.922
rule.

55:47.920 --> 55:50.880
When people gain enough weight
they tend to gain weight all

55:50.880 --> 55:52.980
over the body,
but when people begin gaining

55:52.978 --> 55:55.478
weight it's more likely for
women to distribute it below the

55:55.478 --> 55:56.748
waist, men above the waist.

55:56.750 --> 56:01.220
There's good and bad news for
both men and women in this.

56:01.219 --> 56:05.389
The good news for the men,
is that the weight stored above

56:05.385 --> 56:09.545
the waist seems more readily
mobilized when people go on a

56:09.552 --> 56:10.212
diet.

56:10.210 --> 56:13.650
The bad news is that it's worse
for your health to have the

56:13.653 --> 56:16.093
weight stored in that part of
the body.

56:16.090 --> 56:18.190
For the women,
the weight stored below the

56:18.190 --> 56:20.960
waist seems little--it's more
difficult to mobilize.

56:20.960 --> 56:24.960
It's protected more heavily by
the body, it's defended if you

56:24.961 --> 56:27.421
will;
but the good news is that it's

56:27.422 --> 56:31.212
not going to create risk for
disease as much as the excess

56:31.208 --> 56:33.398
weight stored above the waist.

56:33.400 --> 56:37.160
As I mentioned before,
the reason that people think

56:37.164 --> 56:40.744
this is the case,
is that the fat below the waist

56:40.735 --> 56:44.795
is a storage depot for energy in
preparation for pregnancy,

56:44.800 --> 56:47.490
for all the energy needed to
sustain a pregnancy,

56:47.489 --> 56:50.049
and then to feed the child,
breastfeed the child after

56:50.050 --> 56:50.390
that.

56:50.389 --> 56:53.469
The female is burning
tremendous number of calories

56:53.474 --> 56:56.564
doing these sort of things as
you might imagine;

56:56.559 --> 57:00.259
and that is the part of the
body where the fat for that

57:00.257 --> 57:02.447
purpose has the energy stored.

57:02.449 --> 57:08.029
Because the reproduction is so
much--is such an important

57:08.027 --> 57:12.157
function to defend,
the body defends that way in

57:12.159 --> 57:16.599
particular compared to the apple
shape that might occur above the

57:16.596 --> 57:17.216
waist.

57:17.219 --> 57:20.229
So a very interesting part of
biology of that.

57:20.230 --> 57:23.090
Let's talk about genes and body
weight.

57:23.090 --> 57:26.990
How much is body weight
affected by genes?

57:26.989 --> 57:31.649
Well it's affected a lot by
behavior of course-how much one

57:31.646 --> 57:32.206
eats.

57:32.210 --> 57:35.770
It's affected a lot by culture,
I mean we've got rampant things

57:35.766 --> 57:37.886
like obesity in the United
States,

57:37.889 --> 57:40.569
you've got almost none of it in
a country like Somalia,

57:40.570 --> 57:44.610
but then biology is also an
important player potentially.

57:44.610 --> 57:48.830
I'd like to have you guys do a
little vote with your clickers

57:48.829 --> 57:52.409
and I'd like you to
estimate--now somebody responded

57:52.414 --> 57:56.214
already before I've described
the question but maybe it

57:56.213 --> 57:57.693
doesn't matter.

57:57.690 --> 58:02.340
How much of the variation in
body weight in a population is

58:02.336 --> 58:04.256
explained by genetics?

58:04.260 --> 58:09.130
Now that doesn't mean--so 0 to
25% doesn't mean that 25% of

58:09.126 --> 58:13.986
people who are overweight are
overweight because of genetic

58:13.992 --> 58:15.002
reasons.

58:15.000 --> 58:19.570
What it means is that in a
population that either weighs a

58:19.574 --> 58:22.744
lot or a little,
how much is driven by genetics,

58:22.742 --> 58:25.972
and as weight varies within a
population how much is driven by

58:25.974 --> 58:26.614
genetics.

58:26.610 --> 58:30.640
Do you think its zero to 25%,
which would mean genetics are

58:30.639 --> 58:33.209
having a pretty small
contribution;

58:33.210 --> 58:36.200
25% to 40%;
40% to 70%;

58:36.199 --> 58:39.109
or 70% to 100%,
that would mean genetics is

58:39.112 --> 58:42.652
having a very strong
contribution to body weight?

58:42.650 --> 58:47.130
Okay.

58:47.130 --> 58:48.260
Let's see.

58:48.260 --> 58:49.600
Everybody done voting?

58:49.599 --> 58:51.479
Let's see what your graph says.

58:51.480 --> 58:57.030
14% of you said zero to 25%.

58:57.030 --> 59:01.370
The majority or more then
anything else said 25% to 40%,

59:01.369 --> 59:04.709
40% to 70%, 39% and then a few
said 70% to 100%,

59:04.710 --> 59:07.130
so most of you are clustered in
the middle somewhere.

59:07.130 --> 59:08.940
Well let's see what the number
really is.

59:08.940 --> 59:12.760
There are several prominent
researchers who have done

59:12.762 --> 59:15.632
studies on genetics and body
weight,

59:15.630 --> 59:18.890
and you'll hear some of them
referred to later in the class,

59:18.889 --> 59:22.299
and I'm going to show a video
clip today that has one of the

59:22.297 --> 59:23.567
figures interviewed.

59:23.570 --> 59:26.990
This is Rudy Leibel who is a
very well known physician

59:26.994 --> 59:30.684
researcher at Columbia
University who has studied this.

59:30.679 --> 59:34.119
This is Albert Stunkard at The
University of Pennsylvania who

59:34.121 --> 59:37.051
did the initial human genetic
studies on body weight

59:37.047 --> 59:40.137
regulation and Claude Bouchard
is at a place called The

59:40.144 --> 59:43.364
Pennington Institute and he's
one of the world leaders on

59:43.356 --> 59:44.616
these as well.

59:44.619 --> 59:47.559
II'm going to show you some
data from studies that these

59:47.559 --> 59:49.109
folks and others have done.

59:49.110 --> 59:52.330
There are different methods for
studying the genetic

59:52.326 --> 59:54.026
contribution to anything.

59:54.030 --> 59:59.530
If you're interested in how
much genes contribute to hair

59:59.534 --> 1:00:01.514
color,
to depression,

1:00:01.512 --> 1:00:04.402
to schizophrenia,
to heart disease,

1:00:04.400 --> 1:00:07.710
to cancer, or to body weight
regulation these are the kind of

1:00:07.706 --> 1:00:09.576
methods that typically get used.

1:00:09.579 --> 1:00:12.199
First, you can do animal
studies where you can actually

1:00:12.195 --> 1:00:14.705
go in and measure genes,
you can do that in humans of

1:00:14.713 --> 1:00:15.443
course too.

1:00:15.440 --> 1:00:19.630
Or you can manipulate genes by
breeding animals to be a certain

1:00:19.634 --> 1:00:20.044
way.

1:00:20.039 --> 1:00:22.299
You'll see some examples of
that.

1:00:22.300 --> 1:00:26.660
You can do family studies to
see how well--how much certain

1:00:26.659 --> 1:00:28.839
things cluster in families.

1:00:28.840 --> 1:00:32.190
Let's just say you look to see
whether--if parents are

1:00:32.193 --> 1:00:35.553
overweight whether they're more
or less likely to have

1:00:35.547 --> 1:00:38.467
overweight children,
and so you can see the

1:00:38.472 --> 1:00:39.942
clustering in families.

1:00:39.940 --> 1:00:43.500
Now that's easy to do from a
research point of view but has

1:00:43.498 --> 1:00:45.808
weaknesses as well,
as you can imagine because the

1:00:45.811 --> 1:00:47.611
families also share culture and
environment, not just the

1:00:47.606 --> 1:00:47.956
biology.

1:00:47.960 --> 1:00:55.360
Adoption studies are an
interesting issue here as well.

1:00:55.360 --> 1:01:00.960
That's when you look at people
adopted versus their biological

1:01:00.956 --> 1:01:03.156
relatives who are not.

1:01:03.159 --> 1:01:06.109
Identical and fraternal twins,
twins reared together and

1:01:06.106 --> 1:01:09.426
apart, and then of course you
can go in and do gene studies.

1:01:09.429 --> 1:01:11.249
Now I'd like to describe a
couple of these.

1:01:11.250 --> 1:01:14.980
First, there are ways you can
breed obesity and in this case

1:01:14.983 --> 1:01:18.653
there is a laboratory strain of
mice called the OB/OB mouse

1:01:18.653 --> 1:01:21.793
that's bred to be obese,
so you can look at the genes

1:01:21.791 --> 1:01:22.771
that are doing this.

1:01:22.768 --> 1:01:24.908
Here are more examples of that
kind of thing.

1:01:24.909 --> 1:01:27.589
These have been used for years
in laboratory studies.

1:01:27.590 --> 1:01:30.390
I'd like to show you this
little video clip,

1:01:30.389 --> 1:01:32.609
in this case,
Rudolph Leibel,

1:01:32.610 --> 1:01:36.180
the researcher I mentioned is
interviewed,

1:01:36.179 --> 1:01:39.439
but it talks a little bit about
this issue, about biology.

1:01:39.440 --> 1:01:40.340
>

1:01:40.340 --> 1:02:43.480


1:02:43.480 --> 1:02:45.230
Interesting here that he talks
about cultural perceptions,

1:02:45.230 --> 1:02:47.920
that we accept the fact that
height is very genetically

1:02:47.922 --> 1:02:50.822
determined but less so with
weight because of the way we've

1:02:50.815 --> 1:02:52.955
been trained to think about this
issue.

1:02:52.960 --> 1:02:55.630
But in fact,
biology is an important player.

1:02:55.630 --> 1:02:58.860
One of the first studies done
with humans on this was done by

1:02:58.858 --> 1:03:01.608
Stunkard, one of the researchers
I mentioned before,

1:03:01.605 --> 1:03:03.215
in the country of Denmark.

1:03:03.219 --> 1:03:07.249
Denmark keeps very careful
records of adoptees and for

1:03:07.250 --> 1:03:11.970
research reasons keeps track of
people who are adopted later in

1:03:11.965 --> 1:03:15.915
their life and also the parents
who raise them,

1:03:15.920 --> 1:03:17.880
and the parents who gave birth
to them,

1:03:17.880 --> 1:03:19.610
the biological versus adopted
parents.

1:03:19.610 --> 1:03:22.570
Stunkard, collaborating with
Danish researchers,

1:03:22.565 --> 1:03:25.705
was able to look at these
adoptees, people who were

1:03:25.708 --> 1:03:28.098
adopted at birth;
and then as adults,

1:03:28.097 --> 1:03:31.357
how much they weigh and looked
to see whether their weights

1:03:31.360 --> 1:03:34.040
were closer,
more closely resembled those of

1:03:34.041 --> 1:03:37.321
their biological parents or the
parents who raised them.

1:03:37.320 --> 1:03:40.360
If the relationship was strong
between the adoptees' adult

1:03:40.358 --> 1:03:42.648
weights and the parents who
raised them,

1:03:42.650 --> 1:03:45.230
it would be a strong argument
for culture and environment.

1:03:45.230 --> 1:03:48.020
If the relationship is stronger
with the biological parents,

1:03:48.021 --> 1:03:50.011
of course then it would argue
for genes.

1:03:50.010 --> 1:03:54.350
The results look like this.

1:03:54.349 --> 1:03:57.909
If we look at the right first,
the relationship between

1:03:57.913 --> 1:04:01.813
weights of the children and
their mothers and fathers--their

1:04:01.806 --> 1:04:05.896
adoptive mothers and fathers--as
the weights increase here from

1:04:05.898 --> 1:04:09.528
thin to the median level,
to overweight and obese you

1:04:09.527 --> 1:04:12.277
don't see a very clear
relationship because as the

1:04:12.284 --> 1:04:15.704
adoptees are getting heavier,
the parents aren't necessarily

1:04:15.701 --> 1:04:16.171
heavier.

1:04:16.170 --> 1:04:19.110
So this would suggest that
there's not a very strong

1:04:19.110 --> 1:04:22.740
relationship between the parents
who raised the children and the

1:04:22.744 --> 1:04:25.704
weights of the adoptees;
whereas you get a different

1:04:25.697 --> 1:04:27.477
picture for the biological
parents.

1:04:27.480 --> 1:04:30.440
As the weights are going up in
the adoptees,

1:04:30.440 --> 1:04:33.570
the weights of the biological
parents are higher too,

1:04:33.570 --> 1:04:36.040
even though there had never
been any contact after birth.

1:04:36.039 --> 1:04:39.569
So this would be a strong
argument for biology.

1:04:39.570 --> 1:04:43.870
Another very interesting way of
looking at this is comparing

1:04:43.867 --> 1:04:47.797
monozygotic and dizygotic twins:
identical twins versus

1:04:47.800 --> 1:04:50.820
fraternal twins,
so of course twins born at the

1:04:50.820 --> 1:04:53.560
same time to the same parents,
raised in the same

1:04:53.559 --> 1:04:56.179
environment--but the monozygotic
twins,

1:04:56.179 --> 1:04:58.199
the identical twins,
share all their genetic

1:04:58.202 --> 1:04:58.722
material.

1:04:58.719 --> 1:05:02.139
The dizygotic twins share half
of their genetic material.

1:05:02.139 --> 1:05:05.729
You can look at how the weights
of twin pairs relate to each

1:05:05.731 --> 1:05:09.021
other, how the twins relate to
each other in weight.

1:05:09.018 --> 1:05:12.408
If biology is a player you'd
expect a stronger relationship

1:05:12.411 --> 1:05:15.161
for the monozygotic twins then
the dizygotic.

1:05:15.159 --> 1:05:17.759
In fact that's what you find.

1:05:17.760 --> 1:05:22.230
This is a picture showing twin
pairs of dizygotic twins,

1:05:22.230 --> 1:05:24.990
so twins born same time to the
same parents,

1:05:24.989 --> 1:05:27.349
but share half their genetic
materials,

1:05:27.349 --> 1:05:29.579
and each panel here shows one
pair of twins,

1:05:29.579 --> 1:05:31.269
so this would be one pair,
another pair,

1:05:31.268 --> 1:05:31.528
etc.

1:05:31.530 --> 1:05:35.360
You can really see pretty
striking differences in--some in

1:05:35.356 --> 1:05:38.376
height but also in the amount of
body fat,

1:05:38.380 --> 1:05:41.660
how-- and also other physical
characteristics,

1:05:41.659 --> 1:05:43.969
so you see big differences
here, you see big differences

1:05:43.965 --> 1:05:44.465
here, etc.

1:05:44.469 --> 1:05:48.649
Then if you look at a picture
that shows monozygotic twins who

1:05:48.648 --> 1:05:52.828
share all their genetic material
then what you tend to get are

1:05:52.827 --> 1:05:55.087
mirror images of one another.

1:05:55.090 --> 1:05:58.220
It's another method of
estimating the genetic

1:05:58.217 --> 1:06:00.347
contributions to this issue.

1:06:00.349 --> 1:06:02.839
So genes are a player to be
sure.

1:06:02.840 --> 1:06:06.790
I'm going to skip over this and
conclude about some of the

1:06:06.788 --> 1:06:07.548
genetics.

1:06:07.550 --> 1:06:12.240
Those of you who voted for 25%
to 40% were correct.

1:06:12.239 --> 1:06:15.559
That is the amount of variance
in the population that you can

1:06:15.561 --> 1:06:16.781
explain by genetics.

1:06:16.780 --> 1:06:19.410
Now the question is,
is that a lot or a little?

1:06:19.409 --> 1:06:21.799
Depends on your perspective.

1:06:21.800 --> 1:06:26.050
If the culture believes that
body weight is completely under

1:06:26.048 --> 1:06:30.388
personal control,
or driven by environmental

1:06:30.385 --> 1:06:34.015
factors,
then hearing this number sounds

1:06:34.016 --> 1:06:37.126
like a lot;
that the biology is really a

1:06:37.134 --> 1:06:41.044
player and we weren't expecting
it to be that way.

1:06:41.039 --> 1:06:45.629
On the other hand,
this means that less then half

1:06:45.632 --> 1:06:51.472
of the explanation of population
body weight is biological and

1:06:51.471 --> 1:06:52.621
genetic.

1:06:52.619 --> 1:06:56.629
There's at least 60%,
and as much as 75% left over to

1:06:56.630 --> 1:07:00.410
explain--be explained by
cultural type factors.

1:07:00.409 --> 1:07:04.829
It depends on your perspective,
but here are some of the

1:07:04.833 --> 1:07:09.663
conclusions that people have
raised putting genetics and body

1:07:09.661 --> 1:07:11.271
weight together.

1:07:11.268 --> 1:07:15.258
Genetic influences largely
determine whether a person can

1:07:15.260 --> 1:07:18.290
become obese,
but it is the environment that

1:07:18.288 --> 1:07:22.248
determines whether a person does
become obese and that extent of

1:07:22.253 --> 1:07:23.263
that obesity.

1:07:23.260 --> 1:07:27.140
Then another similar quote,
Genes load the gun and the

1:07:27.143 --> 1:07:29.493
environment pulls the trigger.

1:07:29.489 --> 1:07:33.109
So you may have very little
obesity in a country like

1:07:33.114 --> 1:07:37.164
Somalia where starvation and
famine become real issues,

1:07:37.159 --> 1:07:40.179
but if their environment became
like ours,

1:07:40.179 --> 1:07:43.289
then genetics might determine
how much weight an individual

1:07:43.286 --> 1:07:46.336
would gain but the environment
would determine whether the

1:07:46.340 --> 1:07:48.430
whole population is gaining
weight.

1:07:48.429 --> 1:07:52.059
We'll come back and talk about
culture and environment later.

1:07:52.059 --> 1:07:55.139
I'd like to end with a
discussion of something on taste

1:07:55.143 --> 1:07:58.803
aversions because this also is a
very interesting phenomenon that

1:07:58.800 --> 1:08:01.600
has to do with the way our body
handles food.

1:08:01.599 --> 1:08:05.509
How many of you have a strong
aversion to at least some food?

1:08:05.510 --> 1:08:09.580
Okay, most of you said yes.

1:08:09.579 --> 1:08:12.929
Now my guess is that if we had
you write down what those foods

1:08:12.927 --> 1:08:16.167
are it would highly different
from one another because these

1:08:16.166 --> 1:08:19.126
things tend to get conditioned
in by happenstance.

1:08:19.130 --> 1:08:21.440
You tend to eat a certain food
when you're sick,

1:08:21.435 --> 1:08:24.275
the body then doesn't want you
to eat that food anymore.

1:08:24.279 --> 1:08:27.019
There's a good evolutionary
reason for that of course,

1:08:27.020 --> 1:08:29.630
because in nature if you eat
something that makes you sick it

1:08:29.626 --> 1:08:31.186
means that it's not good for
you,

1:08:31.189 --> 1:08:34.249
and there's potentially toxins
and poison in that,

1:08:34.250 --> 1:08:37.310
and so you want to avoid that
food as much as possible.

1:08:37.310 --> 1:08:42.190
A very interesting example of
this was done with coyotes and

1:08:42.188 --> 1:08:42.848
sheep.

1:08:42.850 --> 1:08:45.430
In the Western part of the U.S.

1:08:45.430 --> 1:08:49.180
the sheep farmers are plagued
by coyotes eating the sheep and

1:08:49.180 --> 1:08:52.180
this is--this becomes a real
problem for them.

1:08:52.180 --> 1:08:54.610
So the question is how do you
control this process?

1:08:54.609 --> 1:08:58.839
The farmers and--the sheep
farmers and the animal

1:08:58.837 --> 1:09:03.327
protection people don't like
each other so much,

1:09:03.328 --> 1:09:05.708
because the farmers want to
kill the coyotes and the animal

1:09:05.711 --> 1:09:07.601
protection people don't want
this to happen.

1:09:07.600 --> 1:09:11.380
A psychological researcher came
up with a very interesting way

1:09:11.376 --> 1:09:13.846
to deal with this,
having to do with food

1:09:13.853 --> 1:09:14.723
aversions.

1:09:14.720 --> 1:09:18.970
What they did was they took
sheep--like carcasses of dead

1:09:18.970 --> 1:09:22.540
sheep that the coyotes would
ordinarily eat,

1:09:22.538 --> 1:09:25.488
and laced the carcass with
lithium chloride--which when

1:09:25.494 --> 1:09:28.944
eaten by the coyotes would make
them very sick--It wouldn't kill

1:09:28.943 --> 1:09:31.953
them but it would make them
throw up and be nauseous and

1:09:31.953 --> 1:09:34.203
feel terrible,
and all kinds of stuff like

1:09:34.198 --> 1:09:34.908
that.

1:09:34.908 --> 1:09:39.138
What they found then is the
coyotes wouldn't eat the sheep

1:09:39.137 --> 1:09:39.877
anymore.

1:09:39.880 --> 1:09:43.330
The same kind of food aversion
that you guys may have

1:09:43.332 --> 1:09:47.192
experienced got drilled into the
coyotes by this particular

1:09:47.185 --> 1:09:48.775
aversive experience.

1:09:48.779 --> 1:09:52.769
There are video tapes--I tried
to get one but I couldn't find

1:09:52.770 --> 1:09:56.430
one--of coyotes coming up to
baby lambs and running away

1:09:56.430 --> 1:10:00.290
whimpering as if that little
lamb was going to do something

1:10:00.288 --> 1:10:03.548
terrible to them because of this
aversion,

1:10:03.550 --> 1:10:06.100
so you can see how strong the
biology becomes.

1:10:06.100 --> 1:10:09.540
Okay, so we will see you guys
on Monday.

1:10:09.538 --> 1:10:12.778
Don't forget to turn in your
concept sheets outside the room.

1:10:12.779 --> 1:10:17.999