0:00

Welcome to STEM Talk. STEM Talk. STEM Talk.

0:02

STEM Talk. STEM Talk. STEM Talk. STEM Talk.

0:04

STEM Talk. Welcome to

0:06

STEM Talk, where we introduce you

0:09

to fascinating people who passionately

0:11

inhabit the scientific and technical frontiers

0:13

of our society. Hi,

0:16

I'm your host, Don Conagas, and joining me to

0:18

introduce today's podcast is Man Behind the Curtain, Dr.

0:20

Ken Ford, IHMC's director and chairman of the Double

0:22

Secret Selection Committee that selects all the guests who

0:24

appear on STEM Talk. Hello, Don.

0:27

Great to be here. So today

0:29

we have a great conversation with Dave

0:31

Feldman, and Dave is the founder of

0:33

the Citizens Science Foundation, which is a

0:35

nonprofit established to support projects and research

0:37

that promote collaborative efforts across the spectrum

0:40

of disciplines, both inside and also outside

0:42

formal scientific institutions. Dave has

0:44

a background in software engineering, but

0:46

his journey studying the effects of

0:49

nutrition on his own metabolic well-being

0:51

led him to a world of

0:53

self-experimentation and a passion for communicating

0:55

science. Before we get to our

0:58

interview with Dave, we have some housekeeping to

1:00

take care of. First, we really appreciate all

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of you who have subscribed to STEM Talk,

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and we are especially appreciative of all the

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wonderful five-star reviews. As always, the Double Secret

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Superfan. The review reads, I

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am so excited to finally

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have discovered this incredible podcast.

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I love learning about cutting

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edge science. As an emergency

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room doc, I most often

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listen to field-specific podcasts, broadening

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my awareness of research across

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other fields through this show

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is so enjoyable, especially learning the

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fascinating biographies of the researchers. As

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a mother of older school age.

2:00

kids, I'm always telling my kids

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that most people follow a non-linear

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path to their careers. So keep

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an open mind about career options.

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I love the skillful way Don

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interviews these amazing scientists to describe

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the unique path that brought them

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to the interesting areas of work.

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Keep up the good work. I'm

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being you listening to Ketchup. Well,

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thank you so much, Splash 888, and thanks

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to all of our other Stem Talk listeners

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who helped them talk to become such

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a great success. Okay, and now on to

2:31

today's interview with Dave Feldman. Hi,

2:41

welcome to Stem Talk. I'm your host Don

2:43

Kornagas, and joining us today is Dave Feldman.

2:46

Dave, welcome to the podcast. Thank you for

2:48

having me. And also joining us is

2:50

Ken Ford. Hello, Don, and

2:52

hello, Dave. So Dave, let's start

2:54

by learning a little bit more about how a

2:57

software engineer with a background in game design came

2:59

to be so enmeshed in the science and pathology

3:01

of lipoproteins. So we're going to start at the

3:03

beginning. What kind of kid were you? I

3:07

was definitely a very nerdy kid.

3:11

I love to play with Legos.

3:13

I love to once I got

3:15

my first computer, which was a

3:18

Commodore 64, I love to code

3:20

on it. I like to build

3:22

things, but like many engineers were

3:24

sort of known more for taking

3:26

things apart. So I enjoyed getting

3:28

on the inside of

3:30

things that looked simple externally,

3:32

like a clock. And then sure enough,

3:34

you get underneath it. And on the

3:36

inside, there's all these gears and wires

3:39

and other things that could be involved.

3:41

And that was always very interesting to

3:43

me. So you

3:45

shared with us that your parents were, and I'm

3:47

quoting this happily divorced when you were five, and

3:49

that you grew up splitting your time between Denver

3:51

and Wichita. So what were the best

3:54

parts of your childhood? Well,

3:56

actually, I would I would say that I

3:59

enjoyed connecting. with others, as

4:02

you mentioned, because I was kind of coming back

4:04

and forth between two households in two different states,

4:06

that taught me the

4:08

importance of playing well

4:10

with other kids because I might only be

4:13

seeing them for a portion of my time.

4:15

So building relationships was, I think,

4:17

very instructive in helping me to understand

4:19

the importance of collaboration and

4:22

getting projects going that also could be

4:24

started and then also stopped at a

4:26

later point as well. I

4:28

understand that you've described your parents

4:31

that you just mentioned both as

4:33

graphic artists and a bit of

4:35

bohemian sort of hippie spirit types.

4:37

What did you learn from them

4:39

that ended up fueling your interest

4:42

in science or other interests? Well,

4:45

yeah, that's part of what's ironic is that

4:48

I would say from both of

4:50

them, I learned the importance of

4:52

kindness. I think that how you

4:54

treat others will pay big

4:56

dividends for how you'll be treated and kind.

4:59

But I'll concede they probably

5:01

weren't as much into science as I

5:03

would ultimately become. One

5:05

of the things about understanding engineering,

5:07

especially at a physical level, is

5:10

getting a sense of how science

5:12

can explain physics, for example. You're

5:14

getting back to the clock, you

5:16

care about things such as friction.

5:18

But at the same time, I

5:20

appreciated their approach to finding

5:23

out things that are

5:25

in the unknown and better understanding how

5:27

to pursue that and keeping

5:29

that wonderment alive, that sort of curiosity.

5:32

That's something that I've loved about both

5:34

my parents is that they've always been

5:36

curious spirits. I

5:39

think that's wonderful for a child to

5:41

have parents that are inquisitive and curious,

5:43

and it typically transfers to the child

5:45

as well. Earlier, you mentioned

5:48

that you took great comfort in taking

5:50

things apart, and that seems to be

5:52

a common theme, actually, among some of

5:54

our guests. And you mentioned the cherished,

5:56

you called it a cherished, Commodore

5:59

64. I remember my

6:01

first computer outside of the military was

6:03

a very early Apple II, you know,

6:05

before they came assembled. You know, it

6:07

was way, way, I don't think it

6:09

was called Apple II then, but it,

6:12

you know, it was like a motherboard and you

6:14

bought parts. And so I understand

6:16

the excitement of those days, you know,

6:19

loading the game programs from a cassette

6:21

tape. I don't know if yours worked

6:23

that way. There weren't disc drives yet.

6:26

The floppy drives weren't around yet for

6:28

personal complaints. No, I

6:30

was doing exactly the same thing. That

6:32

was just the one way in which

6:34

you could record and keep data for

6:36

later. There was an adapter that I

6:39

would buy, which you could adapt

6:41

in a just a normal commercial cassette

6:44

tape player. And it

6:46

was exciting to me that this

6:48

was even a thing that I

6:50

could repurpose what was normally recording

6:52

voice for recording data. And I

6:54

had to understand how this magnetic

6:57

tape could contain these ones and

6:59

zeros that would become so important

7:01

for me to retrieve later. And yes,

7:03

once once the floppy disk drive came,

7:05

I was in heaven because I thought,

7:08

wow, this has got so much more

7:10

data that I can now store and

7:12

retrieve. Yeah, same experience here.

7:14

Sort of marvel of it all. And

7:16

do you remember the sound the cassette

7:18

tape made? It made this weird sort

7:20

of data sound when you

7:23

actually listened to it in an audio

7:25

frequency. It was unintelligible,

7:27

of course, but it had this strange

7:29

sound. But those were very exciting days

7:32

for computers. And somehow these little pathetic

7:34

computers, I think mine was 48K,

7:36

boosted to 64K with an extra

7:38

board. Seems so exciting compared to

7:41

the big mainframes that I was

7:43

dealing with and the computers in the

7:45

military. These seem to have so much

7:47

promise and so exciting. It

7:49

really was an exciting time in that the

7:52

concept of personal computing seemed so foreign

7:54

at first to everyone. And all of

7:56

a sudden it was, wow, we could

7:58

we could have. the power of

8:01

what these companies that were

8:03

usually paying the money to do for us

8:06

right here at home. There's something

8:08

like word processing, which we take so

8:11

for granted now, just being

8:13

able to type something that then is

8:16

stored in some electronic fashion that you

8:18

could then distribute in some electronic fashion

8:20

was revolutionary in the 80s

8:22

to be done at home. And it's

8:24

kind of neat to just reflect back

8:26

on not only that time then, but

8:28

just how fast technology has moved in

8:30

such a short span of time for

8:32

all of us in our lifetimes. Right.

8:35

The first word processors I remember were

8:38

uppercase only on my machine. And

8:40

you had to get different hardware

8:43

to get the lowercase distenders, the

8:45

lower pieces of the letters. It

8:48

was an interesting time. So

8:51

Dave, your early interest in computers also came about

8:53

in a time that predated the internet. So

8:56

what did you learn in those early days? And I'm

8:58

just getting things like learning to write code and building

9:00

games and exploring bulletin board systems. Is

9:02

that right? That's right. So

9:04

you would go and meet other

9:07

likewise computer geeks. Often

9:10

there would be what you might

9:12

call back then meetups, but they

9:15

were like regular weekly sessions.

9:18

And I'd started hearing about this

9:20

BBS that was, everyone

9:22

was getting excited about bulletin board

9:24

systems. And you really could say

9:27

this was the precursor to the internet. What

9:29

you would do is you would get a

9:31

modem on your own home computer that would

9:33

call via phone,

9:36

another computer that was

9:38

stationed at somebody's house. And they would

9:40

have their computer set up so that

9:42

it would be waiting for phone calls.

9:45

And then they had a modem on their end. And

9:48

once the call was connected, and

9:50

it's the same sound you're probably used to

9:52

from AOL back in the days, then

9:55

suddenly you were connected to that

9:57

computer and you could access things

9:59

on the internet. that computer but usually was some

10:01

kind of program that was set up to handle

10:03

that. And that was a bulletin

10:06

board. And the bulletin board system was

10:09

the software that we were all connecting with.

10:11

And then we could like write a message

10:13

and then post it knowing that other people

10:15

who are going to call in later would

10:18

see what we posted. And those were the

10:20

earliest versions of chatting. And

10:23

it was electric. We were all excited except

10:25

the problem was it could only take one

10:28

call at a time. So we would regularly

10:30

be checking to see if the line was

10:32

open so that we could log in. And

10:34

then in order to manage it, you would

10:36

only get a certain amount of minutes that

10:39

you could be on any given bulletin

10:41

board system for your account

10:43

per day. That was

10:45

the very earliest stages, I would

10:47

argue, of this precursor

10:49

to the internet. And as

10:51

you've mentioned, this is all back in the early 1980s.

10:54

And you said you didn't have ready access

10:56

to computer science curriculum until high school. And

10:58

of course, high school computer science courses typically

11:01

contain almost nothing that a computer

11:03

scientist would recognize as actual computer

11:05

science. So it's a good thing then

11:07

that you like self-directed learning, it would seem,

11:09

I'm guessing. So how did you engage your

11:11

interest in programming? Well, that's

11:14

where it gets fascinating. I

11:16

didn't really take a lot

11:18

of computer science courses, either

11:20

in high school or in college,

11:22

because I actually enjoyed the

11:24

self-directed learning more. I wasn't good about

11:27

structured learning. I like to, like many

11:29

engineers, I'm a kinesthetic learner, which is

11:31

I learn a lot more by doing

11:33

and I kind of want to go

11:35

at my own pace. And while that's

11:37

very commonplace now, there's a lot of

11:39

internet courses and so forth, where you

11:41

can kind of set your own pace

11:43

and learn at your own speed. This

11:45

was not how things were done

11:47

back then. So it was great to

11:49

be able to get a book and then actually

11:52

be able to acquire the software or other tools

11:54

myself and learn on my own. And that's pretty

11:56

much what I had been doing since the first

11:58

time I was in college. I had a

12:00

hold of a computer. I liked to

12:04

trial and error my way through many

12:06

different things. And

12:08

that was something that's been

12:10

a constant throughout my life. There are

12:12

many different things throughout both

12:15

my childhood and into young

12:17

adulthood that I dabbled and

12:19

was interested in trying out thinking, hey,

12:21

this coding, this engineering thing is sort

12:23

of fun, but maybe that's not really

12:25

going to be my career. And it

12:27

wasn't really until the dot-com boom that

12:29

I realized that probably was the direction

12:31

I needed to go. It

12:34

would seem that your interest in

12:36

self-directed learning that we've been discussing,

12:38

in addition to helping you learn

12:41

about computers and particularly about programming,

12:43

also has really enabled your current

12:45

interests in your current work just

12:47

as well. We'll return to

12:49

that shortly in our discussion, but it

12:52

seems that that same style of learning,

12:54

as you mentioned, has been sort of

12:56

a theme for you throughout your life.

12:59

Absolutely. And I have to

13:01

say, it is

13:03

a skill like so many other skills. You get

13:06

used to working on

13:08

your own toward goals that

13:11

interest you, recognizing what limitations come

13:13

with that. When you don't have

13:15

somebody there that's, for example, filling

13:17

in a lot of gaps of

13:19

the foundation that would typically come

13:21

with structured learning, then that can

13:23

be a con, but it can also

13:25

be a pro. There's downsides, obviously, in that

13:27

you might have holes in your knowledge from

13:30

what might be more typical of the introductory

13:32

direction, but by the same token, and this

13:34

kind of gets down to some foundations of

13:36

science, by the same token, you might also

13:38

get biased by a number of

13:41

existing assumptions that

13:44

come with structured learning. And

13:46

so there's something kind of nice about coming from

13:48

the outside and not

13:51

having a lot of these preconceived

13:53

notions such that you

13:55

have a little bit of fresh eyes on the

13:57

new thing you're approaching. That's true. I think that's true.

14:00

And you picked early in your life

14:02

one of the ideal things to learn

14:04

that way, which is programming. Maybe not

14:06

computer science in an academic discipline, but

14:09

programming is an ideal thing for the

14:11

person with the right attitude and the

14:13

right outlook for self-directed learning because every

14:15

program you write is an experiment in

14:18

a sense and you get

14:20

feedback quickly on your experiment. So

14:22

I think that kind of topic

14:24

really was an ideal first go-around

14:26

for you and learning something on

14:28

your own and then you applied that

14:31

same sort of attitude and spirit to other

14:33

things. Absolutely. So

14:35

Dave, speaking of skills development and kind of thinking

14:37

about things outside of the box, in terms of

14:39

hobbies, you mentioned that you were a bit of

14:42

a runner and that you also enjoyed forensics. And

14:44

when we say forensics, this is a kind of

14:46

competitive storytelling, not the criminal science version that might

14:48

first come to mind. Can you tell us a

14:50

little bit more about that? Yes.

14:53

So first on the running front, that

14:55

was a bit later in life and

14:57

was with the influence of my now

14:59

wife. She had kind of

15:01

gotten me into running. I am by

15:04

no means very competitive on that

15:06

front. We'll have to come back

15:08

to that later. But definitely during

15:10

high school and college, I enjoyed

15:12

what is typically called forensics there.

15:14

Now, normally when you hear forensics, you

15:17

think of CSI, forensic pathology, and

15:19

that's not what this is. If

15:23

you don't know it, it's an interesting

15:26

competitive storytelling and

15:29

or competitive debate. But

15:32

it's what a lot of us in school

15:34

were coming together for and would then visit

15:36

other schools. And so for example, you might

15:38

have, let's say a 10

15:40

minute comedy that's like a one man

15:43

show. You actually get a piece

15:45

of material. It has to be published material,

15:47

maybe from a play, something like that. And

15:49

like a one man act playing multiple parts,

15:51

you are then playing the different parts. You

15:53

have 10 minutes to do it. And a

15:55

judge is taking down notes that they're going

15:57

to give you later and you're up against

15:59

say. four or five other

16:01

competitors in the same, I

16:04

forgot what they were called at that time session or

16:06

something like that. And you do this for several different

16:08

rounds, and then you might break

16:10

into finals if you had the highest judging scores

16:12

of those of your competitors, and then eventually you

16:14

might win awards. It was great

16:17

in many respects because it taught me some

16:19

degree of discipline. It also sort of rewarded

16:21

that self-directed learning because we're not all doing

16:23

the same thing. In fact, your selection process

16:26

of what you chose to read and perform

16:28

was a lot more up to you, which

16:30

was great. But then there's the other component

16:32

which is in that same forensics

16:35

discipline is debate. And

16:38

forensic debate is very popular in both

16:40

high school and in college, and does

16:42

teach you to interact

16:44

it. You have to collaborate with your

16:46

debate partner, but also you have to

16:49

be good about making a case and

16:51

defending your case. And this

16:53

really drew forward the importance of critical thinking.

16:55

So I learned quite a lot from forensic

16:57

debate and really appreciated what it brought to

16:59

me. I

17:02

can see that would be very helpful

17:04

throughout your life. And even dealing with

17:06

technical subjects and colleagues and teammates working

17:09

on a project, your ability to express

17:11

your ideas clearly and in the structure

17:14

of an argument as opposed to in

17:16

the structure of just assertions. It's frustrating

17:18

when your colleagues on a team passionately

17:21

make assertions, but there's not an argument.

17:25

It's just an assertion. And so I think

17:27

that must have been helpful to you. It

17:30

absolutely is crucial to anticipate

17:34

rebuttals. If

17:37

I'm going to make an assertion, just like you

17:40

said, if I'm going to make a claim, the

17:43

sky is orange. I should already be

17:45

anticipating what the pushback is gonna be.

17:48

If I can go out and double

17:50

check my own work, oh, actually the sky is

17:52

not orange. And I anticipate people

17:54

are gonna be pushing back on. It may seem

17:56

self-evident to some degree, but

17:59

I believe any good. scientists, for

18:01

that matter, should anticipate

18:04

what their, they should be the best

18:06

critic of their own work.

18:08

They should know what the best arguments

18:10

are against what they're wanting to assert

18:12

because they've built that muscle in their

18:14

mind of thinking critically

18:17

about their own work most of

18:19

all. Right. And you have

18:21

to do that when you're constantly facing

18:23

debate, when you have to hold up

18:26

a proposition and

18:28

do your best to defend that proposition, knowing

18:30

full well what we argued against it, because

18:32

in a way you already are doing

18:35

it yourself. You already are your opponent

18:37

inside your mind. And that allows you

18:39

to better structure and understand and present

18:41

your ideas. Right, right.

18:43

I think that's just as important as

18:45

being ready to deflect a rebuttal is

18:47

to be able to express your ideas

18:49

in a form that rises to the

18:51

level of an argument as opposed to

18:54

just a barefaced assertion. So

18:56

Dave, you initially attended film school at Colorado

18:58

University at Red Rocks, carrying on a passion

19:00

in your family of movies. However, I understand

19:02

that your side hustle of doing contract software

19:05

work at night sort of overtook your original

19:07

plan to graduate and move on to the

19:09

movie business. Is that right? Yeah,

19:12

so it was the joint CU Red Rocks film

19:14

program. It was a great program. I

19:16

loved it. It was a fascinating contrast

19:19

though, because while I was interested in

19:21

possibly pursuing this and not many people

19:23

know this about me, that there was

19:25

this phase where I was interested in

19:27

becoming a writer director. I'm taking these

19:29

classes and there's all these starving artists

19:31

on top of instructors that many

19:34

of which had gone to Hollywood or some other

19:37

place where they were trying to shoot film. It didn't work

19:40

out for them. And they had come back and

19:42

said, well, this is the one thing I can do

19:44

is I can at least teach you all about it.

19:46

But just so you know, it's very difficult. It's a

19:48

very difficult business to be in contrast that within the

19:51

evenings, I had

19:53

so much development work, I not only could

19:55

write code as I was mentioning from earlier,

19:57

but I had gotten into a lot of

20:00

of the what at the time was big,

20:02

which was multimedia. There was

20:04

director, I think it was macro media

20:06

director at the time and flash, flash

20:08

was kind of coming up big, but

20:10

also lots of other tools that worked

20:13

with video. And it was, again,

20:15

all of these things we take for granted

20:17

now, but just being able

20:19

to make something that's interactive where you

20:22

could feel like you were immersed into

20:24

a program where you're clicking one thing

20:26

or another and lots of interaction takes

20:28

place around it. That skill was very

20:31

valuable and it was paying me really

20:33

well such that it

20:35

became harder and harder to justify trying to

20:37

work my way through film school. And

20:40

instead, maybe I should stick to

20:43

the software development because it just seems to

20:45

keep paying such large dividends for the time I'm

20:48

putting in. So this takes

20:50

us to 2008. And this is

20:52

when you moved to Las Vegas to work in

20:54

what was the lucrative gaming platform business. So while

20:56

it was financially rewarding to help create platforms that

20:58

allowed people to, and this is just an example,

21:01

play Blackjack on their phones and chat with other

21:03

players while doing so, you said that that wasn't

21:05

really personally fulfilling, but what did you learn from

21:07

that period? It's

21:09

fascinating because when

21:12

you usually say game designer, people think

21:14

of a video game they may play

21:16

on, you know, PlayStation 3,

21:18

but gaming, as they often call

21:20

it here in Las Vegas, is

21:23

these software platforms that are doing

21:25

exactly what you described, where you

21:27

could play possibly for money, or

21:29

maybe it's social gambling, where there's

21:31

actually some social component to it.

21:34

But yeah, the

21:36

non-fulfilling aspect of it was,

21:38

I just wasn't really that much into the

21:40

subject matter. I'm not as

21:43

much into slots or playing Blackjack on

21:45

my own time. And it

21:47

also didn't feel as productive. It didn't

21:50

feel like I was really contributing in

21:52

a strong way to the needs of

21:54

society, not that I think that was

21:57

a necessity, just that there

21:59

wasn't a lot more. could do. So whenever I

22:01

wanted to do something charitable, I would say

22:03

write a check. But all of that said,

22:05

I made many great engineering friends here. And

22:09

I did feel like it was an exciting time

22:12

because from a business and entrepreneurial standpoint,

22:14

I was enjoying a lot of success.

22:16

Things were moving up pretty quickly and

22:19

it put me in a good position for what

22:22

comes later. Speaking

22:24

of what comes later, we now move from

22:26

2008 to 2015 when you receive

22:28

what you describe as

22:34

the piece of paper that changed my

22:36

life. What can you tell us about

22:38

this piece of paper? This sounds intriguing.

22:41

Yeah, so a little bit of setup. For

22:44

two years in a row, 2014 and 2015,

22:46

I had gotten blood work

22:50

that had something concerning me. The

22:53

marker called hemoglobin A1C is

22:56

well known for seeing if

22:58

you're diabetic. And it had

23:01

come in at 6.1 in 2014. And my doctor

23:03

alerted me

23:07

that he said, you know, this

23:10

actually suggests you're pre-diabetic. And

23:13

that I recognize the word diabetic because it's

23:15

quite rampant in my family. I said, Oh,

23:17

you know what, I think it's just because

23:19

I've been eating a lot of junk food, pushing

23:22

this deadline around development. I'm sure that's what

23:24

it is. The year later, 2015, I was

23:27

stunned. It came

23:30

back at 6.1 again,

23:32

because I thought that I'd

23:34

cleaned up my diet. I thought that I fixed

23:36

what I did from the year before. So the fact that it

23:38

came back 6.1, I got very concerned.

23:40

I started to do research and I came

23:43

across what's called what at that time was

23:45

called low carb high fat, LCHF, keto wasn't

23:47

that big of a thing, but it was

23:49

pretty close to keto anyway. So

23:52

I thought, okay, I'll try this for a little while.

23:54

And so I went low carb high fat, and

23:57

I was stunned at how good I was feeling.

24:00

And felt very confident that I

24:02

was addressing the diabetes because my

24:04

carbohydrates were going down diabetes is

24:07

this is a bit simplistic, but

24:09

basically it's a disease of dysregulation

24:12

of glucose metabolism and At

24:16

least in the case of type 2 diabetes and

24:18

so by just having less glucose I felt confident

24:20

and I would later be proven right that

24:23

my a1c would be coming down. Okay

24:25

while I'm feeling great both

24:28

my dad and my sister they're

24:30

getting inspired inspired by what

24:33

is my success story so far and pretty

24:35

shortly after I start they start and Six

24:39

months later they get their blood work back and

24:42

they're looking good across the board They're

24:44

agreeing that this was just a great move for us

24:47

and our family. I get my

24:49

blood work after them and That's

24:51

when I get that sheet of paper that sheet of

24:53

paper contained all of my blood work Which

24:56

I was excited to see because I knew

24:58

it was gonna look great then

25:00

I see my

25:02

cholesterol levels my total

25:06

and LDL cholesterol had

25:09

almost doubled a Little

25:11

more than doubled I believe I averaged an

25:13

LDL cholesterol of around 120 to 130 Which

25:18

if you know anything about it this LDL

25:20

cholesterol, which is sometimes called the

25:22

fat cholesterol. It's supposed to be under 100

25:26

That's the recommended level and

25:29

so my doctor would occasionally nudge me on the 120 to 130

25:31

that it'd be he'd prefer if it Were

25:33

a little bit lower, but it had now jumped to

25:37

around 240 and That

25:41

just made my heart sank at

25:43

that point. It was very intense for me because

25:46

I Couldn't understand why

25:48

why would my two first degree relatives

25:50

not see their cholesterol jump up as

25:52

much as I did Why was I

25:54

the one? Seeing this massive

25:57

rise and That's

25:59

a day. I'll never forget it was November 27,

26:02

2015, and I decided I would go ahead and get

26:05

a second test because maybe it was just a lab

26:07

error. Maybe there was just something

26:09

that happened with the sample. So

26:11

I ended up getting my second test about two

26:13

weeks later, on December 9. But

26:16

between November 27 and December 9,

26:20

I become obsessed. I can't focus on

26:22

code anymore. I can't focus on hardly

26:24

anything. I have to understand how this

26:26

is possible. And so I start reading

26:30

from scratch everything I

26:32

can around lipidology, the study

26:34

of lipids. Basically, it's

26:36

the science around cholesterol. And this weird

26:38

thing happens where I'm both very

26:41

depressed, almost as if somebody had

26:43

died. I'm just stricken

26:46

with fear while simultaneously massively

26:49

curious. Because the more I'm

26:51

learning about this system in

26:53

our bodies that moves around cholesterol,

26:57

the more it looks like

26:59

a network, the kinds of networks

27:01

that I've worked on my

27:03

whole life in engineering. A lot

27:06

of the principles I was just talking

27:08

about with bulletin board systems that would

27:10

ultimately apply to the internet, the

27:13

internet likewise, has a

27:15

massive array of objects interacting with

27:17

each other. That is

27:20

the system that has cholesterol

27:22

ride sharing alongside it. So

27:25

these two weeks, I'm actually losing

27:28

weight because I'm just eating less. I'm

27:31

afraid of the

27:33

amount of saturated fat I'm consuming, or at

27:36

least fat overall, is going to

27:39

increase my cholesterol. So I get

27:41

to where I'm almost food phobic. And

27:43

I'm eating probably about half what I normally do, and

27:45

I lose a lot of weight. And I'm thinking to

27:47

myself, you know what? This next

27:49

test, this next test will

27:52

be a great test because

27:54

if it really is all

27:56

about this consumption of fat, particularly saturated fat,

27:58

I know that I mean it. so much

28:00

less. And if

28:02

that's true, then my cholesterol should at

28:05

a minimum stay the same, but probably

28:07

drop. I finally get that test. And

28:11

no, it had gone up. It

28:13

had gone up another 100

28:15

milligrams per deciliter. My LDL was now, I want to

28:17

say 330, 340, and my total cholesterol was

28:23

425. And this

28:26

may shock you, but I

28:28

may be the only person ever to

28:31

have experienced some amount of relief

28:34

in seeing that new lab. The

28:37

reason is because right in that moment, I knew

28:39

that there was more to the story. I

28:41

knew that it's not that simple,

28:44

that it's simply the saturated fat that

28:46

was driving up my LDL levels. There

28:48

had to have been something metabolic that

28:51

was changing around these lipid levels. And

28:54

that's the true launching point

28:56

was that December 9th test. That's

28:59

interesting. Did you check LDL-P or

29:01

APO-B at the same time at

29:03

that time? I

29:06

did. That was the first point in

29:08

which I started to get what's known

29:10

as an NMR, or

29:13

nuclear magnetic resonance test for your

29:15

listeners. That's a test that

29:17

does go a bit further than a

29:19

standard lipid panel, which is what I

29:22

was getting, what probably most of your

29:24

listeners are getting. The NMR actually breaks

29:26

out these different particle numbers.

29:29

So LDL-P, which

29:31

is what you mentioned, is the vehicle for

29:34

which LDL-C, which is the cholesterol,

29:36

is found inside of. So LDL

29:39

particle count is

29:41

considered by many lipidologists to be

29:44

a much better marker for cardiovascular

29:46

disease risk than LDL cholesterol, because

29:48

you can have a low level

29:50

of LDL cholesterol, yet have a

29:52

high level of LDL particle counts.

29:54

So that's a low amount of

29:56

cargo of the cholesterol, high amount of LDL

29:59

particle. Naturally, I'm

30:01

hoping that my LDL particle count

30:03

is lower, but it wasn't.

30:05

My LDL-P is likewise

30:08

much, much higher. And

30:10

my ApoB, which is the major protein

30:12

that's on top of that particle, which

30:14

we may get into, that also was

30:17

a lot higher. So

30:20

just to further give reference,

30:23

I'm now talking about my

30:25

having lipid levels that are

30:27

in the top 1% of

30:30

the population. So understandably, this

30:32

would be very concerning to

30:34

any clinician, especially a lipidologist.

30:37

Yes, for sure. And there are

30:40

a group of people that, under

30:42

the same circumstances as you described,

30:44

their LDL-C, unlike you, doesn't

30:46

really rock it up, but their

30:49

LDL-P does. And that's why

30:51

I asked, I know several

30:53

people you would know their names. You

30:55

know, their LDL-C didn't experience what you

30:58

experienced, but when they were

31:00

really seriously doing ketogenic

31:02

diet, their LDL-P doubled

31:04

and sort of got their

31:06

attention. Yes. And to

31:09

be sure, I want

31:11

to qualify that while we may

31:13

be talking about the mechanism I'm

31:15

positing for why I think

31:18

this happens, I always try to take

31:20

a moment to disclaim that we're still

31:22

in the area of theory. For sure.

31:25

So as I would always say, continue to

31:27

work with your doctor, much of what I

31:29

may be describing might give some comfort that

31:32

this may not be as concerning,

31:34

but it's still a developing hypothesis.

31:37

Absolutely. If you don't mind,

31:39

I'll go ahead and posit what I think

31:41

is going on. I believe,

31:43

and this is what was

31:45

kind of, I guess you could say, taught

31:48

to me through my series of experiments that

31:50

would follow, is I

31:52

would, after that December 9th and for the

31:54

next two, three years

31:56

especially, I would continually take my blood

31:59

and... vague

38:00

because it kind of meant anybody who

38:02

sees any increase in LDL. So

38:05

I kind of co-opted that term

38:07

and added lean mass hyper-responder. So

38:10

we sometimes shortened it to just LMHR. And when

38:12

I wrote this article in 2017, I

38:16

honestly didn't know truly

38:18

how common it really would be. And

38:21

as of today, it's still the most commented

38:23

on article because it was an explosion. It

38:26

was like I could really feel it within

38:28

a couple of weeks just how much

38:30

this landed. Because I had no

38:32

idea. There might have been a

38:34

bunch of people who were much more overweight

38:37

who'd say, no, no, I have those cut

38:39

points. I have that triad of very, very

38:41

high LDL and high HDL and low triglycerides.

38:44

So let me tell you what that cut point is for

38:46

anybody listening. It's an LDL cholesterol of 200 milligrams

38:49

per deciliter or higher, an HDL

38:51

cholesterol of 80 milligrams per deciliter

38:53

or higher, and triglycerides of 70

38:55

milligrams per deciliter or lower. And

38:57

just so you know, those three in isolation

39:00

are already very rare on their own.

39:02

It's already very rare to find anybody

39:04

with an LDL, standing LDL that's above

39:06

200 or an HDL

39:09

above 80, for example. So

39:12

those three together and the fact that so

39:14

many people were coming forward to report that

39:17

they had that triad was

39:19

very cathartic. It was

39:21

an extremely impactful experience for me. The

39:24

one footnote I'll throw in though, that became

39:26

very relevant to me in my life later

39:28

was I was just following it to

39:30

its logical conclusion. I kept thinking, you know how this

39:32

relates back to the energy model I just described. And

39:35

I'm writing this at the time in this article, I

39:37

think this might actually end up applying to children.

39:39

Like we don't see a lot of blood work

39:42

from children because they're

39:44

not normally getting cholesterol tests, but

39:46

it would make sense. It would make

39:49

sense that they would be more often

39:51

metabolically healthy. And of course, many

39:53

of them would be lean. And

39:55

that became a big deal because I then

39:57

started getting some parents that were writing in

39:59

who. They

54:00

would say, look, we can all

54:02

agree that's multifactorial. The

54:05

issue is that if we can feel

54:07

confident LDL is a component, then lean

54:09

mass hyper-responders, even if they prove to

54:11

be lower risk, could be even lower

54:14

risk if they had all the

54:16

same things the same, but they had lower LDL. And

54:20

I'm happy to acknowledge that they may be

54:22

correct. The question that I think is an

54:24

interesting and important one that we can answer

54:26

quickly is how big a risk

54:28

are they right now? Because

54:31

there are still going to be populations that

54:33

are going to choose

54:36

to forego cholesterol lowering therapy. And

54:39

I think if we're bringing an answer to

54:41

them, let me give you probably the most

54:43

obvious examples. The people who I know who

54:45

have the highest levels of LDL right

54:48

now, right at this moment, are people like this patient

54:50

in the case report we just talked about where they

54:52

have a very severe medical reason for why

54:55

they want to be on a ketogenic diet. So

54:58

if they get really, really low in carbs and

55:00

they're very lean and they're very

55:02

athletic, they might see levels like this patient,

55:04

this case report where it's say 500, 600, I know

55:08

some that are like 700, 800. I

55:10

tell all of them the same thing, which

55:12

is this is unknown territory. Of

55:14

course, this would be considered very concerning. I

55:16

can't give medical advice, but I think

55:18

a lot of people in this position would

55:21

get CT angiograms so that they would

55:23

at least know where they stand at

55:25

the moment. And maybe there's further follow up

55:27

past that point, of course. But

55:31

do we know enough to say we

55:34

feel confident you cannot

55:36

continue with this diet if

55:38

medication can't bring your levels low enough?

55:41

And that's why I became interested in seeing

55:43

how I could participate in making this happen,

55:47

getting the research around studying

55:49

the risk specifically. It's just,

55:53

that's the one twist to my story is after 2017,

55:56

I was trying to get

55:58

cardiologists and lipidologists who were

56:00

researchers. interested to do this

56:02

study and I couldn't

56:04

make it happen. So in 2019,

56:07

I went ahead and

56:09

founded my own public scientific

56:11

charity. It's Bonafide 501c3,

56:13

where we would just raise the money

56:15

ourselves to pay for the

56:17

research to happen. So

56:20

you touched on this a little bit already, Dave, but back

56:22

in May of 2022, you published

56:24

a paper in the journal Metabolites and

56:26

it was titled, Lipid Energy Model, Reimagining

56:28

Lipoprotein Function in the Context of Carbohydrate-Restricted

56:30

Diets. So can you just explain a

56:32

little bit more for our listeners what

56:34

the lipid energy model is and then

56:36

really expand on its significance? Sure.

56:39

So the major factor

56:41

that we would posit is

56:44

as you're restricting your carbohydrates further

56:46

and further, this ultimately results in

56:49

a lower amount of glycogen stores

56:51

in your liver. And

56:54

as you get below a certain

56:56

threshold, this will ultimately result in

56:58

a cascade of different

57:00

components that activate

57:02

more full body lipid

57:05

turnover. So you have

57:07

something we would refer to as

57:09

hypoleptinemia, which we would later publish

57:12

when we got data on this.

57:15

Leptin levels come way down. This

57:17

can have downstream impacts on

57:19

the HPA axis and thyroid,

57:22

and you have greater release of

57:24

these fatty acids from your adipocytes,

57:27

your fat cells. So as more

57:29

fatty acids are being liberated

57:32

from your fat cells, that means more

57:34

of your surrounding tissue can make use

57:36

of those instead of the glucose that

57:38

they now have less made available to

57:40

them because you're lower carb and higher

57:43

fat. That also means though more of

57:45

the fat that's getting liberated coming into

57:47

the bloodstream is getting picked up by

57:50

the liver. And as it's getting picked up

57:52

by the liver, it's getting repackaged into

57:55

triglycerides, which are the stored form of

57:57

fat, and then placed into new

57:59

V8s. VLDL

58:01

are ApoB containing lipoproteins that

58:04

the liver secretes. A

58:06

higher secretion of these VLDL

58:09

are coming back into the bloodstream and

58:12

they are both repleting those

58:14

fat cells that are liberating the fat at a

58:16

faster rate and also powering

58:18

those same tissues that are using

58:20

the smaller amounts of

58:23

fatty acids. This by the way

58:25

includes especially the heart, cardiomyocytes,

58:28

the cells of the heart in

58:31

particular really like triglycerides

58:33

off of lipoproteins and they

58:36

draw a lot of power off

58:38

of those in particular. So in

58:40

effect you don't just become a

58:42

fat burning machine, you become a

58:44

fat circulating machine and if you're

58:46

metabolically healthy then that's

58:48

why this has

58:51

to work with a larger amount

58:54

of trafficking, a bit more

58:56

of a network of movement for these

58:58

carrier proteins to move this fat around

59:00

in bulk. That's a

59:03

good explanation. One of the other

59:05

things I found interesting about the

59:07

paper published in Metabolites is how

59:09

quickly that journal reacted to the

59:12

submission. The journal says it was

59:14

submitted in 19 April,

59:16

reviewed, then revised, then accepted, and

59:18

then published all in 30 days.

59:20

I was a journal editor long

59:23

ago and maybe because of the

59:25

advent of the internet, internet

59:28

was not used for reviews then

59:30

but FedEx. However,

59:34

that's unbelievably fast so they must

59:36

have a tremendous staff or

59:38

a team of reviewers because I

59:40

can't imagine getting it even reviewed

59:42

in 30 days. So it's remarkable.

59:45

Yeah, we're pretty proud of that paper. It's one

59:47

of the most read papers they have and

59:50

we were going to be very... we

59:52

love being transparent and we were going

59:54

to be transparent with the reviewer comments

59:56

which was approved by all within. There's

1:00:00

almost no way to say this without it coming off as

1:00:02

a humble brag, but our concern

1:00:04

was that the reviewer comments were positive

1:00:07

to the point where it might look like we were doing

1:00:09

that on purpose, like we were trying to signal, but

1:00:13

it was well-received. Thus far,

1:00:15

I have to say one thing that I

1:00:17

take as its own signal is we haven't

1:00:19

had any challenge. We've not had a single

1:00:21

letter to the editor that has

1:00:24

found a corrective point to put forward. This

1:00:26

isn't to say that I don't think there

1:00:28

aren't many more things we need

1:00:30

to do to improve on the model, or

1:00:32

that we may have gotten some fundamental aspects

1:00:34

of it wrong. But we've intentionally reached out

1:00:36

and brought this paper to many lipidologists, some

1:00:39

of them fairly big names in the field.

1:00:42

I've been impressed that

1:00:44

it's been quite

1:00:46

a lot that's been acknowledged thus far, implicitly.

1:00:50

Dave, another paper that we'd like to touch

1:00:52

on by Dr. Isabel Cooper, was

1:00:54

published in December 2023. It's a

1:00:56

study in frontiers and endocrinology titled, Diary Markers

1:00:58

and Body Composition Predict LDL Cholesterol Change in

1:01:01

Lean Healthy Women on a Kiajank Diet, Experimental

1:01:03

Support for the Lipid Energy Model. Could you

1:01:05

please share with our listeners some methods around

1:01:07

this paper, and then also the findings that

1:01:09

were reported in this paper by Dr. Cooper?

1:01:12

Yes. I'll concede to the papers we published

1:01:15

that when I had looked at in a

1:01:17

while. I'm just

1:01:20

more an edit comment. I'm hopeful that I

1:01:22

don't get anything wrong, but I'm going to

1:01:24

try to quote it from memory. I

1:01:26

know this is one I should have anticipated and should have had the

1:01:28

paper in front of me, but give me just one second. I'll see

1:01:30

if I can summarize it in a way that I'm pretty confident. This

1:01:33

was a fascinating paper in that it

1:01:36

was adult women

1:01:38

who were already historically on a

1:01:40

ketogenic diet and then had an

1:01:42

intervention to intentionally bring up the

1:01:44

carbohydrates. Then actually, a further intervention

1:01:46

to bring them back to the

1:01:49

ketogenic diet, and it was tracked

1:01:51

very methodically all the way through.

1:01:53

Really fantastic work because it was

1:01:56

already anticipated by the Lipid Energy

1:01:58

Model that we would like to

1:02:00

see the see an association between

1:02:02

how much their cholesterol levels would

1:02:04

change based on their

1:02:06

existing leanness and metabolic health.

1:02:09

And that's what those data showed, is that

1:02:11

those who had lower

1:02:14

BMI, the leaner they were, the

1:02:16

more likely their LDL would go

1:02:18

down. It would tend to be

1:02:21

starting at a higher amount and

1:02:23

then would go down at a

1:02:25

higher magnitude than those who were

1:02:27

not as lean. But of special

1:02:30

relevance to us was also on

1:02:32

the thyroid. And we saw that

1:02:34

while BMI was predictive, free T3

1:02:36

was also very predictive, how low

1:02:39

free T3 went associated very strongly

1:02:41

with the change in LDL cholesterol.

1:02:44

But the two of them together were

1:02:46

extremely predictive, extremely correlative. So

1:02:49

Dave, earlier you mentioned funding and

1:02:51

self-funding and setting up a 501c3

1:02:54

to essentially almost crowdsource

1:02:56

funding for research. I think that's

1:02:58

exciting. In 2019, just

1:03:00

to give it a name, you founded

1:03:03

the Citizen Science Foundation. Could you talk

1:03:05

a little bit about the challenges you

1:03:07

must have dealt with in establishing a

1:03:10

public charity dedicated to propelling scientific research?

1:03:12

I imagine it was harder than you

1:03:14

first thought. Not-for-profits always are harder than

1:03:17

they look on the surface. Can

1:03:19

you elaborate on this? No

1:03:22

question. It's so much... Anybody

1:03:25

who's considering it definitely

1:03:28

recognize that it's a lot of work.

1:03:30

But I think the part that many

1:03:32

don't realize is just how much uncertainty

1:03:34

there was around all of this. I

1:03:36

didn't know if this would work at

1:03:38

all. As I'm putting together this charity,

1:03:41

I'm not sharing with anybody

1:03:43

what I'm doing because I wanted to do it

1:03:45

all at once. I wanted to announce what we

1:03:47

were going to try to do, which was to

1:03:49

raise money for this study. And hey, now

1:03:52

we have a charity that can take your donations. And it's

1:03:54

a bona fide 501c3, so

1:03:56

your donations are tax deductible. But I don't

1:03:58

really know how... how many people are going

1:04:00

to reach into their pocket and actually put

1:04:03

out money. I came from the world of

1:04:05

technology and while things like

1:04:07

kickstarters were exciting, usually you get

1:04:09

something like you get a game that you

1:04:11

helped to fund. You're

1:04:13

a Kickstarter, so you get one of the

1:04:16

first devices that came from the Kickstarter or

1:04:18

you, and maybe you're,

1:04:20

you're helping to fundraise for an independent

1:04:22

movie and therefore you get tickets to

1:04:24

come see the movie. This was unlike

1:04:26

anything I had ever heard before because

1:04:29

I wanted to go to the community

1:04:31

and say, I need at least $50,000 because I think

1:04:33

it'll be $2,000 per lean mass hyper responder

1:04:37

when I bake in the pricing for

1:04:39

the CT angiograms and everything else. So

1:04:42

I need at least 50,000 and

1:04:44

actually what I really want is 200,000. Well,

1:04:48

one of the first advice, the pieces of

1:04:51

advice that they give you for kickstarters is don't ask

1:04:53

for anything more than 50,000. That's

1:04:55

already a limit. That already feels too far away

1:04:57

for the people who are contributing. Make it, make

1:05:00

50,000 to be your, your

1:05:02

scope and then maybe everything else is a stretch goal.

1:05:04

And here I am, I'm saying not only are you

1:05:06

not going to get a toy

1:05:09

or some kind of product, but we're

1:05:11

getting data. We're,

1:05:14

and we've never done this before. So

1:05:16

we're going to organize a study. We're

1:05:18

going to work with a bonafide

1:05:21

research center. So

1:05:23

that'll be all on the up and up, but we've

1:05:26

never done this before. This is brand new. Put

1:05:28

your trust in me and your dollars to

1:05:30

try to put this together and make it

1:05:33

happen. And I genuinely didn't know once I

1:05:35

got into that Houston stage, whether

1:05:37

I was going to end up with five

1:05:39

bucks or 500 bucks or 5,000 bucks by the

1:05:44

end of the day or by the end of the year. I had

1:05:46

no idea. That does

1:05:49

sound like a daunting undertaking

1:05:51

with lots of uncertainty, but

1:05:53

I'm glad it worked out. And

1:05:55

following up on that, describe how

1:05:57

you came to partner with the

1:05:59

Lundcliff. Institute at UCLA. I think

1:06:01

that might be an interesting story.

1:06:04

Yeah, we were looking around for

1:06:07

how we could best assess

1:06:10

development of atherosclerosis with lean

1:06:12

mass hyperosmonters. And pretty quickly,

1:06:15

we realized the best test

1:06:17

was CT angiography. Doing

1:06:19

these CT angiograms yielded very

1:06:22

high resolution. They've gotten so much

1:06:24

better in a short time. So it used

1:06:26

to be that to get a CT angiogram,

1:06:28

you had to experience as much as say

1:06:31

20 millisievers of radiation.

1:06:33

As a frame of reference,

1:06:36

year to year living, a year

1:06:38

of life, just doing nothing at

1:06:40

all, you get about 3.5 millisievers,

1:06:42

give or take from the sun

1:06:44

and from background radiation. A

1:06:47

mammogram, I believe is something like 0.5

1:06:49

millisievers. And

1:06:51

a chest x-ray is, I think, a lot

1:06:53

less than that. So it's a substantial amount

1:06:56

of radiation dosage. And now they're down to

1:06:58

where I myself have had two CT angiograms

1:07:00

and they were two millisievers each. So

1:07:03

it wasn't a big ask to

1:07:06

see if we could get participants for

1:07:08

the study to get these CT angiograms

1:07:10

given how much resolution

1:07:12

they have to detect not just calcified

1:07:15

plaque, like we discussed earlier, like

1:07:17

a CAC, but also non calcified

1:07:19

plaque at a spatial resolution of

1:07:22

one millimeter or even less in

1:07:24

some cases. That's

1:07:26

amazing. That's like really excellent

1:07:28

data to detect

1:07:30

if they are rapidly developing atherosclerosis.

1:07:34

And we need to find somebody who is

1:07:36

an expert in CT angiograms

1:07:38

and part of who helped us

1:07:41

who really laid down

1:07:43

why they were so good and made that

1:07:45

pitch was Dr. Matt Budoff. We got in

1:07:47

a meeting with Dr. Matt Budoff. He explained

1:07:50

not only were these

1:07:52

scans great, but the original intent

1:07:54

was for this to be a five year study.

1:07:56

We figured it'd have to be five years to

1:07:58

get meaningful enough data. He pointed out

1:08:00

that no, actually one year is more

1:08:03

than enough time, especially at the exposure

1:08:05

levels we're describing. If the LDL cholesterol,

1:08:07

if the LDL particle count, if the

1:08:09

APOB is all at these super high

1:08:12

levels that we've been describing up until

1:08:14

now, given the present limit hypothesis, we

1:08:16

should for sure see a signal of

1:08:18

very rapid developing plaque in our participants

1:08:21

with CT angiograms in one year's time.

1:08:24

And so that was it. Like

1:08:26

lungless looks like the

1:08:29

one we want to work with. And Dr. Matt Boonoff sounds

1:08:31

like the guy to take up the charge.

1:08:34

Sounds like a good connection. Absolutely. What

1:08:36

is the current state of the

1:08:38

foundation? Right now, do you

1:08:41

have a collection of projects it's currently

1:08:43

funding? Well we have

1:08:46

completed the original scope of

1:08:48

this first study that I went

1:08:50

to Crowdfund, which is for the

1:08:53

one I just mentioned for these participants. They've

1:08:55

all had their second scans and we're now

1:08:58

funding or at least raising money

1:09:00

for a second study, a

1:09:02

sister study to the Lean Mass Hypersponder

1:09:05

study, which I'm calling the Triad Study

1:09:07

because it's going to be a more

1:09:09

relaxed criteria than Lean Mass Hypersponders. For

1:09:11

example, the HDL we're going to allow

1:09:13

for being lower, the triglycerides higher and

1:09:15

possibly more risk factors. Actually

1:09:18

the first one we're doing was

1:09:21

more restrictive in that you

1:09:23

couldn't have existing cardiovascular risk factors. It

1:09:26

already was difficult to get through an IRB to

1:09:29

say we're going to study these folks who have

1:09:31

very high levels of LDL cholesterol who are refusing

1:09:33

treatment to find out how high a risk they

1:09:35

are. And I think we now have enough data

1:09:37

that we can go back to these same IRB

1:09:39

and say we now want to do a trial

1:09:42

on folks who are

1:09:44

more at risk than our original study's

1:09:47

participants. But that also

1:09:49

makes it more real world. It

1:09:52

makes it more likely that's a larger

1:09:54

population you would see. You apply to

1:09:56

more people. So following up on that,

1:09:58

how do you see? and the foundation

1:10:00

evolving, and how would you like it

1:10:03

to evolve? I would love,

1:10:05

frankly, I would really love to just

1:10:08

see this model play out all over

1:10:10

the place, not just with ours, but

1:10:12

with others, because crowdfunded science is bringing

1:10:14

us answers. We're, frankly, we're

1:10:17

just not likely to get otherwise, because

1:10:19

there's pretty much, and I was told this

1:10:21

early on, there's pretty much two channels to

1:10:23

research funding. One is,

1:10:25

of course, the coveted NIH, which is government

1:10:27

funding, but you're kind of at the whims

1:10:30

of whatever they determine is

1:10:32

worth funding, or

1:10:35

some form of business

1:10:37

model related funding. There has to be

1:10:39

some company involved. There has to be

1:10:41

some interest, and typically that means there's

1:10:43

some product that's going to get sold.

1:10:46

So when pharmaceutical companies are interested in

1:10:48

a drug, it takes

1:10:50

a lot of money to get to

1:10:52

that first pill that's ready to go

1:10:54

to market, and therefore a lot of

1:10:56

expense, but there needs to still be

1:10:58

something that has a return on that

1:11:00

investment. And by definition, our

1:11:03

return on investment is data and

1:11:05

nothing else. We're trying to

1:11:07

just get good data to get to the answers. And

1:11:09

so I think that we're proving

1:11:12

this works. We did succeed

1:11:14

at crowdfunding. That $200,000 I

1:11:16

told you, we've raised much more than that since

1:11:18

that announcement. And we've,

1:11:22

I think, managed to achieve pulling this

1:11:24

off through a lot of good graces.

1:11:26

For example, we have no

1:11:28

admin overhead. We don't take

1:11:30

any money personally. Nobody gets compensated who's a

1:11:32

part of this organization. The only

1:11:35

overhead we have are for third-party services

1:11:37

like credit card processing or hosting or

1:11:39

something like that. But that allows us

1:11:42

to correctly advertise that it's practically

1:11:45

like a tunnel. You're donating to

1:11:47

us, which goes straight to the

1:11:49

research. So Dave, I

1:11:51

know you touched on this a little bit earlier, and I

1:11:53

also know that you're eager to share some news about a

1:11:55

study that your team has just completed on 100 Lean

1:11:59

Mass Hyperresponders. in which subjects received a

1:12:01

CT and geogram to establish their baseline

1:12:03

coronary artery status, and then another one

1:12:05

a year later. So could

1:12:07

you describe the findings in terms of

1:12:09

atherosclerotic plaque in your group compared to

1:12:11

the Miami Heart Study Group, and also

1:12:13

just talk about the findings of that

1:12:15

study in general? Yeah, I'll have to

1:12:17

kind of set the stage a little

1:12:19

bit more because the study that I've

1:12:21

been describing up until now, the one

1:12:23

that the initial one we've crowdfunded for,

1:12:25

which I'll just call the Lean Mass

1:12:27

Hypersponder study, its original endpoints were longitudinal

1:12:30

and just against themselves. So there's no

1:12:32

control group. It's 100 scans

1:12:35

on those meeting our eligibility criteria. So

1:12:37

100 of them all getting one scan

1:12:39

and then getting a second scan one

1:12:41

year later against their own timeline. So

1:12:43

whenever we fly them to Lundquist, they

1:12:45

get their scan and then they're scheduled

1:12:47

to come back a year later to

1:12:49

get their second scan. And then we

1:12:51

look at a population level at what

1:12:53

their plaque differences are. And

1:12:57

as we were starting this, we

1:13:00

knew it'd be difficult to get a

1:13:02

control group that's attached because it's actually

1:13:04

very difficult to find folks who are

1:13:06

metabolically healthy, lean, and do not have

1:13:08

this hyper response, which frankly, seems to

1:13:10

be supporting the model is that it's

1:13:12

so hard for us to find a

1:13:14

control group. But even a mixed diet

1:13:16

control group was basically

1:13:18

non-existent at the point

1:13:20

that we were planning this. So that's

1:13:23

an important piece of information

1:13:25

in that another amazing cardiologist,

1:13:27

Karam Naseer, was

1:13:30

at that time starting to

1:13:32

conduct what's known as the Miami Heart

1:13:34

study. And the Miami Heart study, in

1:13:36

that study, they have 2400 participants,

1:13:39

much more than our 100, but they're only

1:13:41

getting a single scan. So it's

1:13:43

cross-sectional and then they follow up. Well,

1:13:46

that's fine. I'm still

1:13:48

excited about the prospect of being able

1:13:51

to do a match analysis where we

1:13:53

actually take our initial 100 scans

1:13:56

and look at them against a

1:13:59

matched control group. group out

1:14:01

of the Miami Heart cohort. And

1:14:04

that's what we were able to do. That's

1:14:06

what this first analysis that got reported on

1:14:08

was able to accomplish is once

1:14:10

Karam Nasser, who's the principal investigator of

1:14:12

Miami Heart, was able to authorize the

1:14:15

sharing of their data with Lundquist, the

1:14:17

statistician within Lundquist then went forward to

1:14:19

see if she could put together a

1:14:22

match and indeed was able to put

1:14:24

together an excellent match. Basically, there were

1:14:26

of our 100, there were 80 that

1:14:30

matched the age

1:14:32

requirements of the

1:14:34

Miami Heart cohort. Ours

1:14:36

was more liberal, theirs was a bit more restrictive.

1:14:40

And so of those 80,

1:14:43

once that was isolated, she would

1:14:45

then go see if there could

1:14:47

be a randomly acquired group within

1:14:49

the Miami Heart cohort that could

1:14:51

become the matched control that as

1:14:53

closely as possible matched age, matched

1:14:56

ethnicity, matched pretty vascular disease risk

1:14:58

markers, etc. And it's extremely tight.

1:15:00

It's very impressive. The average age

1:15:02

was 55.5 and both. The ethnicity

1:15:06

is a perfect match across the

1:15:09

way, the blood pressure, inflammatory

1:15:11

markers, they're all like nearly identical.

1:15:13

It's extremely close. Probably the most

1:15:15

notable difference is the EMI is

1:15:17

lower for our group, but that's

1:15:19

not surprising, which kind of bears

1:15:21

back to the model and so

1:15:23

forth. But also both groups had

1:15:25

very high HDL cholesterol,

1:15:27

lower triglycerides, our cohort

1:15:30

was a bit more pronounced,

1:15:32

which isn't surprising. But here's the

1:15:34

important part. The LDL indeed was very

1:15:36

different by design. The LDL for the

1:15:39

matched cohort is 123,

1:15:41

which as I mentioned earlier, is like right

1:15:43

in the average. The LDL levels for our

1:15:46

80 for this match analysis was

1:15:49

272. So again, we're

1:15:51

well into the top. We're actually in the top 10% of

1:15:54

the top 1% of the population. And the

1:15:56

average time for our

1:15:58

cohort is 45. 4.7 years so

1:16:00

4.7 years an average LDL of 272

1:16:05

milligrams per deciliter and once the

1:16:07

match was done the plaque was not

1:16:10

Statistically significantly different between

1:16:13

both cohorts interestingly the

1:16:16

Cohort from our study the lean

1:16:19

mass hyper spotters actually trended toward

1:16:21

lower plaque overall there were

1:16:23

more that had zero plaque than the

1:16:25

Miami heart group and There

1:16:27

were less that had plaque and double digits

1:16:30

I I believe ours had total plaque score

1:16:32

of just one that was in the double

1:16:34

digits Whereas they had I want to

1:16:36

say something like four now I

1:16:39

want to immediately follow up by saying

1:16:41

there's still not a statistically significant difference

1:16:43

and therefore people shouldn't overinterpret

1:16:45

that because The

1:16:48

only reason it's relevant information is if it

1:16:50

was trending in the opposite direction I think

1:16:52

many would assume that the lean mass hyper

1:16:54

spotted cohort may not show a statistically significant

1:16:57

difference in plaque But they were on their

1:16:59

way like that was already

1:17:01

in the work So maybe that they would be

1:17:03

lower risk, but they even so they were already

1:17:05

showing a breakaway from where

1:17:07

the Miami heart cohort Came

1:17:10

in so that's that's kind of a bit

1:17:12

of a summary, but that was the first glimpse ever

1:17:15

of a decent size

1:17:17

number of these people that

1:17:19

I've been speculating on for eight

1:17:21

years now that I'm one of and

1:17:25

What their corresponding plaque levels would be a

1:17:27

baseline at least and how that would compare

1:17:29

it to another? Baseline group

1:17:31

that's in a typical mixed population, but

1:17:33

also with likewise low cardiovascular risk markers

1:17:36

That is interesting. Now. What will

1:17:39

the results be published soon? Yes,

1:17:41

it's actual in fact, it's funny It's should

1:17:44

I thought there was a chance it might be published

1:17:46

before this took place Before we

1:17:48

were recording, but I'm pretty sure by the

1:17:50

time this is released. It'll be it'll be

1:17:52

out and published. No, that'd be great So

1:17:55

all this said you've managed to bring your love of

1:17:57

film back into your life through the documentary that you're

1:18:00

working on. So can you tell us a little bit about that? Yeah,

1:18:02

it's pretty exciting. Jen

1:18:05

Eisenhart is, she's

1:18:07

also been long in this business

1:18:09

of being a documentarian. And she's

1:18:11

filmed a lot before she also

1:18:13

filmed fat fiction. And a friend

1:18:15

connected us and we started

1:18:18

deciding to capture a lot of what

1:18:20

was going to take place as it

1:18:23

happened regarding both this study and

1:18:26

going into this background that my story and

1:18:28

how this all came about. That's being put

1:18:30

together right now. We, I

1:18:34

think got our first footage around,

1:18:37

I want to say the beginning of last year. And

1:18:40

it's been kind of exciting because basically

1:18:43

it's following not just this

1:18:45

study, but all of

1:18:47

these individual stories of people going

1:18:49

on a ketogenic diet. And particularly

1:18:51

those folks that have had

1:18:54

efficacy who, like Nick

1:18:56

Norwitz, like Jen

1:18:59

Unwin, who's another person we interviewed,

1:19:02

they have particular medical

1:19:04

reasons for which a ketogenic diet is

1:19:06

of special relevance, but also who are

1:19:09

dealing with this factor, this extraordinarily high

1:19:11

level of LDL cholesterol and whether or not

1:19:13

this is going to be a high

1:19:15

risk for them. And

1:19:17

I think, I think

1:19:19

it's going very well. Obviously a lot

1:19:22

of it's being written as it's happening,

1:19:24

including this final analysis with the longitudinal

1:19:26

data, because beyond the match analysis, the

1:19:28

piece still yet to come is that

1:19:30

comparison of the first hundred scans with

1:19:33

the second hundred scans. And while

1:19:35

I want to continue to manage everyone's expectations that I

1:19:37

do believe there'll be an increase in plaque as there

1:19:39

would be with any group of 55 year olds,

1:19:42

will it be a pronounced increase? Will

1:19:45

we see the kind of levels that we would

1:19:47

expect with the lipid hypothesis? Well, this documentary is

1:19:50

capturing that in real time,

1:19:52

including these findings. All right.

1:19:54

So we're going to close our time together

1:19:56

by asking the obvious question. Every film student

1:19:58

should be asked your face. My favorite film is the 1995

1:20:00

crime classic, The

1:20:03

Usual Suspects, directed by Bryan Singer. So we're

1:20:05

gonna ask you to defend your choice. Yes,

1:20:09

I really enjoyed that film for

1:20:11

many reasons because

1:20:14

I think it is

1:20:16

a great example of setup and payoff, which

1:20:19

I think all the best films, all the best stories really

1:20:22

can, you've probably heard of

1:20:24

Chekhov's Gun. Generally speaking,

1:20:26

if you're a very careful

1:20:28

storyteller, you are

1:20:31

excellent at rewarding the audience over

1:20:33

and over again for not

1:20:35

only the setups they see, but

1:20:38

the setups they don't. And I

1:20:40

think The Usual Suspects does a really good job.

1:20:42

Without spoiling it, there's almost an

1:20:44

entirely different experience in watching the film the

1:20:46

second time around because now you're watching more

1:20:48

for those things you might not have noticed

1:20:51

the first time. Melissa,

1:20:53

fantastic answer. I'll have

1:20:55

to check that film out. I

1:20:57

think I haven't seen a film

1:20:59

in a movie theater since the

1:21:01

original Star Wars. I think it was 1976.

1:21:06

I found the audience unbearably

1:21:09

rude. So this

1:21:11

would be a film to watch, I think, on a

1:21:13

computer. I'll

1:21:15

concede it, it hasn't aged well

1:21:17

with regard to both

1:21:19

the director and one of the actors in

1:21:22

it. But if you

1:21:24

can set that part aside as a piece of

1:21:26

film work, it's

1:21:28

quite excellent. Sounds interesting. Well,

1:21:31

Dave, it's been fantastic chatting with you today about the

1:21:33

work you're doing. And thank you so much for joining

1:21:35

us on Stem Talk. Thank you

1:21:37

for having me. Absolutely, I appreciate

1:21:39

it, Dave. Stem Talk.

1:21:41

Stem Talk. Stem Talk. Stem

1:21:43

Talk. Stem Talk. So

1:21:46

that was such a great conversation with Dave.

1:21:48

I really think that the work he's doing

1:21:50

with Citizen Science Foundation is a really interesting

1:21:52

avenue to pursue. And I really do believe

1:21:54

that the foundation's collaborative spirit is refreshing and

1:21:56

something that's really needed in this field. I

1:21:59

agree, Dawn. And I hope

1:22:01

the foundation continues to be impactful.

1:22:03

It's off to a good start.

1:22:05

We certainly need more research funding

1:22:07

opportunities, beyond only those provided by

1:22:09

the government. Agree if you

1:22:11

can. So if you enjoyed this interview as much as

1:22:13

Ken and I did, we invite you to visit the

1:22:15

Stem Talk webpage where you can find the show notes

1:22:17

for this and other episodes at StemTalk.us. This

1:22:20

is Don Contega signing off for now. And

1:22:22

this is Ken Ford saying goodbye until

1:22:24

we meet again on Stem

1:22:26

Talk. Thank

1:22:31

you for listening

1:22:34

to Stem Talk. We

1:22:37

want this podcast to be

1:22:39

discovered by others. So please

1:22:41

take a minute to go to iTunes to

1:22:44

rate the podcast and perhaps even write a

1:22:46

review. More information about this and other episodes

1:22:50

can be found at our website, StemTalk.us.

1:22:52

There you can also find more information

1:22:55

about the guests we interview. Thank

1:23:01

you.