Presentation on CDC research program from 1999 to CFSCC

I don't think some of these findings were ever published.

I'm clearing out some of the mass of paper I have and thought I should highlight it somewhere

https://listserv.nodak.edu/cgi-bin/wa.exe?A2=ind0910C&L=CO-CURE&P=R2735


CFSCC meeting, on April 22 (1999).


DR. REEVES: The objectives of the CDC's chronic fatigue

syndrome program are to: determine the pathogenesis of CFS, testing

the hypothesis that the syndrome represents the outcome of several

unrelated initial insults; to estimate the magnitude of the problem,

that is to say, prevalence and incidence in the Unite States; to

define the natural history of CFS; to identify risk factors and

diagnostic markers; to provide current, appropriate technical

information on CFS to government agencies, public health officials,

health care providers, patients, and the public; and ultimately, to

develop control strategies.

Within the CFS Program we attempt to address these objectives

within the context of patient concerns. And current patient concerns

of highest priority, with particular relevance to CDC's CFS Program,

include: patient care -- that's diagnosis and detection, treatment,

rehabilitation, appropriate "third-party" coverage; to conduct

research to identify the cause of illness and diagnostic markers;

estimates of the prevalence and incidence of CFS; a revision of the

case definition and the name; and definition of CFS characteristics

-- prevalence and clinical features -- in underserved populations,

particular interest on children and racial-ethnic minorities.

What I want to do in this talk is to summarize where we've been

since I last presented. We're doing a large study in Cedric County,

which is Wichita. The primary objective of this study is to estimate

the prevalence of CFS and other fatiguing illnesses in the Wichita

population. We selected Wichita in part because it is generally

representative of the United States.

There are four secondary objectives. First, we follow study

participants annually to better understand the clinical course of CFS

and to estimate the incidence of CFS and other fatiguing illnesses.

We use information collected from study participants to explore

empirically derived case definitions. Clinical specimens from study

subjects are subjected to various laboratory assays. And finally,

this study will allow comparison of population-based prevalence

estimates with those previously derived from the physician-based

surveillance.

I want to address the baseline survey & clinical evaluation.

The baseline survey was a 3-stage investigation composed of screening

and detailed telephone interviews followed by clinical evaluation.

From March through August of '97, random digit dialing was used to

screen households and obtain information on fatiguing illness.

Approximately 90,000 people were enumerated, which represents about a

quarter of Wichita's population, so this was a very large

population-based study. Approximately 6,500 people who reported

fatigue lasting a month or longer and a similar number of

non-fatigued controls were asked to complete a detailed telephone

interview.

Finally, the clinical evaluations were performed on 300

subjects who reported symptoms which were criteria for CFS and when

we detect someone who meets the case definition on interview, we call

that CFS-like illness, and 64 non-fatigued subjects.

At the October, 1998 meeting, I reported preliminary CFS

prevalence estimates. At that time, we had sufficient data to

classify 39 of 300 clinically evaluated fatigued subjects as CFS. We

have now completed follow-up clinical studies on subjects that were

left as pending at first clinical evaluation, we really did not know

whether to rule them out or not, and we've identified a total of 49

CFS patients at base-line.

We used statistical methods to account for our sampling design,

and our current estimates are that 238 per 100,000 adults 18-69 years

of age in Wichita have CFS. This translates into about 700 cases in

Cedric county. Women accounted for the majority of cases, with a

prevalence of 356 per 100,000; and most of these cases occurred in

white women, resulting in a prevalence of 394/100,000. This is close

to a half of a percent. These are very, very high numbers,

significantly higher than we had thought in the past.

With respect to adolescent prevalence, the telephone survey

also obtained information on fatiguing illness in adolescents.

Because of limitations imposed by Human Subjects Committee

considerations, we could only conduct detailed interviews and

clinical evaluations on adolescents 12-17. Seven adolescents with

CFS-like illness were clinically evaluated and none had CFS.

However, -- so it is very hard to calculate a prevalence.

However, telephone interview data and US census estimates

allowed us to estimate the prevalence of CFS-like illness in children

or in adolescents -- and that was 338 per 100,000. This is about a

fifth the rate of CFS-like illness in adults, which was 1,623 per

100,000. If we assume that the proportion of CFS to CFS-like found

in the adult population, which was 15%, could be applied to

adolescents, we would estimate -- or we do estimate now that 50 per

100,000 adolescents 12-17 years of age in Wichita have CFS.

The first year of follow-up study occurred between February and

October of last year. The objective was to collect updated

information on the health status of the 13,000 first-phase

respondents.

Used a telephone interview which was very similar, somewhat modified

from the baseline one. All subjects originally invited to the clinic

at baseline, as well as those newly identified with CFS-like illness

were asked to participate in the second-phase clinical evaluation.

And we are currently calculating incidence rates of CFS and other

fatiguing illnesses.

With respect to current work. Final analysis of Wichita

prevalence data will be completed in this year and a manuscript will

be submitted for publication. Analysis of the data with respect to

empirically deriving case definitions will be completed this year and

a manuscript will be submitted for publication. We want to submit it

to a high-visibility, weekly journal.

Analysis of the data with respect to empirically deriving case

definitions -- case definitions from the data is underway. We will

complete that this year and a manuscript will be submitted for

publication.

Twenty-four month follow-up began in February and is ongoing.

It will be completed this year and we will make decision concerning

continuation for three years.

We have a variety of new analyses that we are actively engaged

in. We obtained a wealth of data from the Wichita study and we are

undertaking a variety of other analyses. The subjects of major

importance to us right now include: an analysis of the burden of CFS

on the population. We talked a lot about this yesterday. We talked

about continuing medical education. Only 9 -- and that's 18 percent

-- of the 49 subjects with CFS that we identified, reported they had

been treated or diagnosed with this illness. It will be extremely

important to describe the utilization of health services by

individuals with fatiguing illnesses.

The relationships between CFS-like and CFS. In all, 1,600 per

100,000 adults were identified with CFS-like illness during a

telephone survey, yet 15 percent, 238 per 100,000 had CFS confirmed

upon standard clinical evaluation. To some extent, that reflects

extremely sensitive screening instruments with low specificity, which

is good. But we are going to need to look at this in detail for some

of the future studies.

Differences in responses obtained in telephone screening and

clinical interview. We have found several instances in which

subjects provided different responses to the detailed telephone

interview and the in-person interview conducted at the clinic. We

are exploring that in detail.

We are very interested in the comparison of passive versus

active surveillance for CFS. Again, this was brought up yesterday,

perhaps, in wanting physicians to report to us. Now we had done that

previously from

'89 to '93. Wichita-based prevalence rates are more than 20 times

higher than those estimated through physician surveillance, and we're

conducting an analysis of that. Interestingly, 15 -- or the 18

percent of patients who sought physicians, if we calculate prevalence

based on that, we calculate exactly the same prevalence we calculated

from physician-based surveillance.

We have hired two epidemiologists to work on these various

analyses and we are advertising a post-doctoral fellowship that we

hope the fill later this year. Those who are academics on this panel

we would invite any suggestions or perhaps people wishing to take

sabbatical at CDC.

We have considered two applicants for the neuroendocrinologist

position. Both had excellent laboratory backgrounds but lacked

adequate clinical and epidemiologic expertise. And we are continuing

to search for a qualified neuroendocrinologist or neuroimmunologist

with epidemiologic expertise.

I'd like to talk about the new study that we are planning. We

are planning a national survey for CFS in the US which we hope to

begin this year. The objective will be to conduct a telephone survey

to estimate sex, age, race, and socioeconomic-specific

prevalence of medically unexplained fatigue, prolonged fatigue of one

to five months; chronic fatigue, one to six months without syndromic

features; and CFS-like illness. Special emphasis in this survey will

be given to identifying fatiguing illness in adolescents and

racial/ethnic minorities.

Specific aims are to determine if findings from the Wichita

study can be generalized to the US population; to estimate the

geographic occurrence of prolonged fatigue, chronic fatigue, and

chronic fatigue syndrome-like illness. In particular, are there

metropolitan, urban, rural differences? Are there

north/south/east/west differences or any indication of cluster. Are

there differences associated with migration? That sort of

information.

Collect information that can be used to verify, complement, and

extend empirically derived case definitions. Collect information

that can be used to estimate the economic burden of CFS-like illness,

which would include utilization of health services with respect to

socioeconomic status or occupation as well as changes in

socioeconomic status/occupation due to illness.

Derive information that can be used in future case control

studies, which would include identifying subjects for future recall,

identifying high-risk areas, and deriving hypotheses for analytic

studies.

And finally, to derive information for use in designing a

national or regional CFS registry.

I'd like to quickly cover what we have been doing in molecular

epidemiologic analysis. We discussed this last time. Classic case

control studies have not consistently identified laboratory markers

or risk factors for CFS. We believe that an open-ended analysis of

differences in gene expression between CFS patients and controls will

give the best opportunity for providing insight into disease -- this

sort of disease with an unknown pathogenesis.

Our initial approach uses high-density filter arrays to

identify differences in gene expression in peripheral blood

lymphocytes, white cells. This is an extremely new technology which

has not yet been applied to epidemiologic studies, and has been

applied very rarely so far to studies of unknown things. Thus we

have had to carefully standardize our assays. I discussed this last

time. We have now optimized sample collection, storage methods, and

labeling methods for chemiluminescent analysis of gene expression.

We have used the assay in relatively simple tissue culture

systems infected with human papillomavirus or HPV, to standardize

both the technique and the analysis methods. HPV has only 7 genes

and their functions are well described. These genes can be up or

down regulated, turned off, on or down, depending on whether the

virus exists free in a free state in the cell, which is called an

episomal state, or whether the viral DNA has fused itself with the

human DNA, which is called an integrated state.

We have measured cellular messenger RNA expression in this

system and are currently utilizing different mathematical approaches

to identify and characterize the differences, and I'm going to show

some of those quickly.

We have also begun to test archived samples from the 1993

Atlanta Case Control Study -- we've got six of them done so far.

We're testing them against four different array formats. So we're

testing gene expressing in a Stress Array, in a Neurobiology Array,

in an Immunology Array, and in a Cytokine Array. Each one of these

tests 588 different genes. We're now looking at new formats that

will test 5000 to 10,000 genes.

What I'm going to do is just -- with some apologies which I

hate to do, for the technical nature of this -- Doctors Komaroff and

Klimas raised some doubts which we shared. This is how this study is

done. A piece of filter paper about half the size of an 8x10 sheet

of paper is used. It has six quadrants. This is just a general

array. One quadrant has onco-genes, two are suppressor genes, stress

response genes, apoptosis which is programmed cell killing, et

cetera. This is just a general filter.

The way in which this is done is that RNA is extracted from the

cells of interest, copies are made in the DNA format. These are

hybridized or put on the filters and allowed to combine and the

filters are then looked at using photographic film.

And this is what a filter looks like. I showed you this one, I

think, last time. This is from our HPV studies. This is normal

cells. These are cells infected with HPV in an episomal form. You

can see that for example, right here, this is very dark. It's not

very dark right there. It's more -- these are exactly the same

except for the two things -- you can see the differences in

expression. This doesn't help anybody at all. It's kind of neat and

I can give a nice talk about it. We can, in fact, quantify it.

And this is an example of the output quantifying this. So for

example, episomal reinfected cells are expressing gene -- a gene in

quadrant A-1b, A-1a, normal cells are not. Here there's the same

amount of expression -- this is the kind of output we get for 588

genes on each filter.

Now, how can we tell the differences in these? There are two

ways, and again, I apologize for this. It looked really, really neat

when I did it on my computer. Doesn't look quite so neat here. This

is the ratio of signal in an episomally-infected cell compared to a

normal cell, and you can see how the genes fall out there. Now what

you do is you look for large differences in expression. And so we

can take those genes that are expressed by episomal but not by forced

enkarytinocytes (ph) and look at each one of those.

We can look at those and you can call these CFS patients, CFS

patients derived by a numeric case definition, sudden onset, slow

onset, et cetera. There would be genes expressed only by those

patients and not by controls; there would be genes that are expressed

by both that are expressed much more by the cases than the controls;

or by the controls than the cases. And one needs to look at those

genes and see what they're doing. This may lead us to hypotheses of

pathogenesis.

As far as a diagnostic marker which I think is much more

exciting or immediate use, we can also compare the overall profiles

of these genes and determine whether we can see differences

mathematically in populations defined by the profiles. And you can

see just looking at these profiles of cell cycle/cell response et

cetera gene, that at least within the known system, there are quite

easily demonstrable differences. This type of analysis could be used

to look at and differentiate between those sorts of systems, and

potentially to determine -- and again, obviously, this is a

population of cells -- if this is reproducible between patients to

determine between patients and non patients.

More interesting yet, and this is coming from a rapidly

evolving technology, mathematical models exist using parsimony

analysis to derive family trees, and this is looking at -- we had no

reason to believe that this would work, except that it did, and it

fit our hypothesis. This is mathematical model of the output of two

arrays which can very clearly distinguish non-infected cells in a

branch of the family tree, cells which have unregulated HPV

expression because of integrated DNA, and intermediate populations

which is due to -- using technical jargon -- multiply integrated DNA

and episomal DNA.

And what we hope to do is to use this sort of analysis, which

is quite different actually. The more genes and the more people you

have, the greater likelihood you have to do it, to derive algorithms

for clearly distinguishing patients with chronic fatigue syndrome or

other fatiguing illness, based on overall gene expression.

DR. KLIMAS: Is there a factor analysis?

DR. REEVES: I beg your pardon?

(continues)

 

(continued)


DR. KLIMAS: Factor analysis on the cluster?

DR. REEVES: I can't give you a great explanation yet because I

barely understand it, but if it works, I promise to explain it

thoroughly at the next meeting.

Just to quickly finish, in addition to our open-ended -- I will

stress that this is currently the most cutting edge approach, I

think, that exists in molecular epidemiology. NIH is using this sort

of technology in a variety of areas. It's rapidly opening up -- it

may or may not work. I think -- it is our opinion that given what

has been tested so far, this has a very good likelihood of finding

something. Again, it may not.

We've had some very intelligent people with some very

attractive hypotheses -- and I could go back to Dr. DiFreitas (ph)

that made absolute sense and did not work out well. So, we do not

know if it will work or not, but we think it's a very appealing

method.

In addition to our open-ended approach to molecular analysis,

we are also collaborating on very specific hypothesis-driven

projects. We have finished a study with Dr. Irwin Gelman, Mount

Sinai School of Medicine to test the hypothesis that alterations in

immune responses of some CFS patients could reflect reactivation of

endogenous retroviruses. Defective latent endogenous retroviruses

are present in many copies in the human genome and if reactivated

could potentially form a marker for altered immune status.

Dr. Gelman has presented and published preliminary data that

expression of the so-called pl5 gene, which is a retrovirus protein,

with known immunosuppressive activity, could be a marker of CFS.

He, unfortunately, had to use patients of convenience with few

controls, so we collaborated with him to provide him with blinded RNA

samples extracted from patients and controls in our Atlanta Case

Control Study. Testing is now complete. He detected pl5-like PCR

products in 26, or 50 percent, of the 52 subjects he tested.

Sixty-two percent of 13 CFS cases had pl5 products, as did

unfortunately 46 percent of the controls. The differences are not

statistically significant.

No demographic or other subject characteristics were associated

with detection of p15. Age distributions of p15 positive and

negative subjects were identical. Fifty-three percent of the 43

women and 33 of the 9 men, not a significant difference, were p15

positive. Fifty percent of the gradual and 71 percent of the sudden

onset cases were p15 positive. Seventy one percent of the patients

described a flu-like onset, and fifty percent of those who did not

had it detected. Overall distribution of current wellness scores

were equivalent between pl5 positive and negative subjects. And we

found no differences in mean duration of illness between the positive

and negative subjects.

We are currently preparing a manuscript to report that we found

no evidence to support the hypothesis that activation of endogenous

retroviruses plays a role in CFS.

Finally, we are investigating possible overlap between

hereditary hemochromatosis and chronic fatigue syndrome. Hereditary

hemochromatosis is a well known syndrome of iron overload. The

end-stage disease is characterized by extensive iron overload in

tissues causing liver cirrhosis, diabetes and joint pain. However,

early symptoms are non-specific and include fatigue and cognitive

impairment.

Therapy, consisting of iron removal by phlebotomy -- bleeding

people, restores normal life expectancy, if begun before the onset of

organ damage. The gene for the condition has been recently

identified, and the mutation is considerably more frequent in the

population than previously recognized. Symptomatic disease is

associated with the homozygous mutation, but not all individuals with

homozygous mutations are affected, so it is an example of a genetic

disease with incomplete penetrance.

We are using transferrin saturation as a phenotypic screen for

iron overload to identify potential hemochromatosis patients in the

Wichita study. 395 samples tested to date, 26, or six percent, had

border-line elevated levels, and 12, or three percent had abnormally

elevated levels, and these levels indicate definite iron overload,

and within hemochromatosis research are essentially considered as

hemochromatosis and confirmed by testing for the gene.

We have contacted affected subjects and offered them the

opportunity to be evaluated for the possibility of hemochromatosis,

and possible treatment. Other studies have described hemochromatosis

in about .06 percent of the population. So we are -- in three

percent of our study subjects, are considerably higher. This is much

higher than what has been done before. It's five-times greater.

We are currently linking the test results to patients' data

files in order to analyze and interpret the results. So we haven't

linked-down yet to cases or controls.

In addition, we are participating in a population-based survey

of Kaiser Permanente HMO patients that will permit correlation of

patients meeting CFS-like illness with mutation status in the

hemochromatosis gene.

And that would be my summary of CDC activities to date.

DR. KLIMAS: So would you recommend that clinicians should be

screened for hemochromatosis on the basis of the Wichita population?

DR. REEVES: We aren't really recommending that yet, and there

is some considerable data actually within CDC -- CDC has a

hemochromatosis program, and they are having their own internal

debates as far as CDC consensus as far as whether hemochromatosis

screening should be perhaps, routine. Though we don't have a firm

recommendation on it yet until we get into our data and talk to the

people who are responsible for that program.

DR. KOMAROFF: A lot of questions. On hemochromatosis, do the

iron and total iron-binding capacity levels which are much more

commonly available to doctors track with this marker for

hemochromatosis?

DR. REEVES: It's my understanding that it's transferred

saturation is what needs to be done as far as the screening test.

DR. KOMAROFF: That is sufficient?

DR. REEVES: That is sensitive for this gene.

DR. KOMAROFF: In terms of the molecular studies, I understand

you to be looking at differences in the patterns of cellular gene

expression, but not to be looking for evidence of non-cellular ...

infectious agent, nucleic acids. You're not using subtractive

hybridization to find non-human sequences, but rather looking for

unusual patterns of human sequence.

DR. REEVES: The latter is on our plate, and I think I talked

about that a little bit last time. We, right now, are trying to

establish -- something we are going to do -- we've built up that

program considerably and right now our major, our major interest is

to try to finalize the analytic method. You, as usual, you and Dr.

Klimas had excellent comments at the end of the last coordinating

committee. We've been worried about that ourselves, so our worry

right now was, can we get analytic methods that we think will work?

We've got three we're working on. Can we use this in patients

-- that's why we're working on the Atlanta Case control patients now

-- can we get through those? We're going to do Gulf War patients

next, and then we'll do the Wichita study and the other stuff is

under active investigation, but I don't have anything really to

report yet. But we will, in fact, be doing that.

DR. KLIMAS: The neurobiology thing is really intriguing to me

because lymphocytes have so much expression of neuro and/or endocrine

substances and receptors for the same, and we understand so little

about why that's there and what it's doing. So I'm really curious

and really hopefully the control subjects will teach us an awful lot

about that.

DR. REEVES: Well, I think it's -- it's interesting to read the

literature. I mean lymphocyte biology is one of the areas in which

expression arrays are being -- you know, in fact actively -- actively

used, and the time between the immune system and neurobiology and

everything, I think, is --

DR. KLIMAS: It's fascinating.

DR. REEVES: Yes, it's very fascinating.

DR. KLIMAS: I was in a meeting last week where this was the

focus of the meeting, and a neurobiologist called lymphocytes the

peripheral brain, and the immunologist called the brain fixed

lymphoid tissue.

MR. CRUM: I have a couple areas that I want to go into -- and

I'm over my head with your presentation here, but with the first

methodology you used, did I hear you correctly that there's some way

that you might be able to identify a pathogen?

DR. REEVES: With the methodology we are using now, we are

looking at host response, and we would not identify a pathogen in

that way. It is possible that the biology of the host response that

we might identify would point to infection or a pathogen being

present. Other methodologies that Dr. Komaroff referred to, and

there are a variety of them, which essentially are equally new, and

on the edge of the envelope of science, are available for detecting

pathogens that have not been well described yet and we are intending

to do that.

It is very, very difficult to prepare a presentation that is

both understandable to the lay person as well as meaningful to the

people that do it, and one always kind of works a -- walks a

tightrope in doing the presentation and one wishes the presentation

to be understandable and at the same time wishes to be able to

present it in enough sophistication to get feedback from people who

do it. And we will try in future -- and in might, at the risk of

being impertinent, not be a bad session for the beginning of one of

these meetings to try to present this sort of methodology in a form

that's understandable for both the lay person as well as to the

experts.

DR. CURLIN: Bill, I agree, I think this is very exciting and

it's being used and applied many places. Are you running a risk,

though, of looking first at the immune gene arrays rather than

something else? Are you really looking -- what led you down that

track first?

DR. REEVES: We are not terribly interested in the immune

arrays. The immune arrays are only one of the arrays that we're

using. The thing that is really intriguing about the peripheral

lymphocyte -- or the peripheral white cell system -- is that white

cells, in addition to their immune function, have and express

neuroendocrine products -- essentially in parallel with the central

nervous system. So they have neuroendocrine functions that are

similar to the immune system.

And our hope, since it's really the only tissue that we can get

-- we have yet to establish a leisure (ph) in CFS -- is to try to get

into a system which has multiple functions -- I mean cytokines and

immune function will be one of those, and these are also tied to

neuroendocrine functions, but the stress arrays which get at a

variety of neuroendocrine, the cells regulatory arrays, the

neurobiology arrays, you know, are all going to be used in this.

But to some extent, it is a fishing expedition.

DR. CURLIN: Alright. A pretty big vat for this fishing

expedition.

DR. REEVES: Yes, and again one -- it is -- it brings to mind

what Dr. Lawrence was talking about yesterday. In essence, we're

doing a -- it's not just a fishing expedition, but it's attempting to

look at can we make correlations that can be evidence-driven type

research, in essence.

I think epidemiologically a major challenge and problem of CFS

is that it has not been terribly amenable to the classic case control

approach, and the classic case control approach has found a lot of

tantalizing things, but for a lot of reasons they have not held up

very well. They have not been able to be -- completely validated

between things. So this is a slightly different approach to that.

DR. CURLIN: A question about your survey which I understood is

going along quite well. Lacking physical findings, other than

observations during the clinic visit, what really is discriminating

features or the part of the clinic visit that separates -- what you

call CFS and CFS-like on the telephone? I mean, still in their view

-- one's long distance and one's visible and in person. Isn't that

basically it?

DR. REEVES: The major thing is that there was a complete

physical examination which would include things like taking blood

pressure, palpitating, and looking for masses, taking a much more

complete past medical history, exploring aspects of the past medical

history, and doing screening laboratory exams.

We will find a fair number -- I mean a lot of patients that

will describe a major surgical procedure in the last year that people

discover a thyroid lump. We will discover hypertension. They -- and

this is a problem that was discussed a bit yesterday -- I forget who

discussed it -- we are trying to do a research case definition, and

so we are stringently classifying people. It is not at all

inconceivable that much of the 1600 per 100,000 individuals

identified with CFS-like illness on the phone -- in fact might be --

might be diagnosed as CFS by their physician, but what we're trying

to do essentially is to link it in to these analytic things, is that

if somebody has an elevated blood pressure, they're ineligible. They

will not be classified as a case.

DR. CURLIN: That's what my point that I'm trying to make is

that basically the exam and the drabs (ph) and so forth --

DR. KLIMAS: Would rule out.

DR. CURLIN: Yes, it's rule outs?

DR. REEVES: Yes, that's correct.

DR. CURLIN: Okay, so it -- I'm asking that because it seems

like for one thing, we dance around on this a little bit, is some

sort of more scientific definition and search for what in the world

is fatigue? And it's not tiredness, and it's not anything else. And

there are other conditions that have what would be the same sort of

fatigue, and I was going to ask are you going to -- you've got some

hemochromatosis patients -- I don't know that much about it, but if a

key component is indistinguishable from the fatigue component, surely

you'd have those kinds of patients that run through your arrays as

well.

DR. REEVES: That's correct. And I think actually one of the

things that becomes interesting about the arrays is that we can

classify subjects any way we want. In other words, we have 300-some

people who had blood collected during their physical exam, and we

have some that will very specifically meet a case definition. We

have others that we have excluded for a variety of reasons.

I did not go into it in detail, but our mathematical modeling

of case definitions, our factor analysis approach, will identify

another type of patient yet. And we -- what we hope to do is

essentially, not only looking at the laboratory data, but looking at

other risk factors as well, is to try to sort out these different

groups of patients and empirically identify patients, strict case

definition patients, psychiatric exclusion patients, a medical

exclusion patient, and see if they're different in any of these

things.

DR. CURLIN: Nancy.

DR. KLIMAS: Two comments. First, back to the issue we were

just talking about, not to forget about the relaxed nature of the

illness, and that particularly refers to lymphocyte studies, that

longitudinal studies might be very helpful.

DR. REEVES: Yes.

DR. KLIMAS: But the other comment's actually completely off

this. Do you have an update for us on the adolescent study awards.

DR. REEVES: What we have elected to do with the adolescent

study, in particular because of our findings of such a low prevalence

in Wichita, is that we have decided that rather than go directly into

schools at this time, it would be best to complete our national

survey which we can do very rapidly, see if we can confirm these

sorts of things and derive perhaps a more precise study design.

The study design that we had considered going into schools to

get a large number of adolescents, if in fact our estimate of Wichita

is correct, we would find very few and in costing this out after we

got into the exact nature of it, we determined it would cost us

between $4 and $5 million to do that, which is the entire budget.

So we have determined that at this time we think that a

national survey with some over-sampling for kids of different

racial/ethnic groups in different parts of the country will allow us

to do a much better job of looking at this in adolescents than going

with the study design that we had previously thought would be best.

DR. KLIMAS: So when would that happen and now what's the

timeline for the adolescents?

DR. REEVES: Right now the timeline for the national survey --

we hope to link this to the national immunization survey which is

ongoing. We had hoped that we could get this done -- and so it's

dangerous to give timelines --

DR. KLIMAS: Particularly to committees.

DR. REEVES: -- particularly to committees. We hope that we

can get this done, if everything goes well, in the -- hopefully in

the January cycle -- they do cycles four times a year, and do some

more serious thinking about what we could exactly do with adolescents

in the year 2000 -- which would be essentially the time, had we been

able to do the other, we would have begun it anyway.

But I think we have some very serious concerns now about the

prevalence rates that we did see about exactly how to get into that

group in the best way.

DR. CURLIN: Thank you, Bill. I think we ought to move on --

 

This is really long, is there anything specific you were referring to?

 

I was rather surprised to read that Dr Reeves said "if somebody has an elevated blood pressure, they're ineligible. They will not be classified as a case."

I have high blood pressure, but otherwise am fairly typical ME/CFS.

 

Here are two things I noted in a post on this in 2009 but I think there may be others

 

Yes, the CDC has used some odd exclusions over the years. If I recall correctly, another was a positive Romberg sign

 

Incidentally, I didn't develop high blood pressure until a few years into ME. So how would Dr Reeves classify such people? When our blood pressure rises, we are miraculously cured of ME? If only!

 

I've related this elsewhere, but hemochromatosis was something my family doctor mentioned when I saw him 2 days after my onset in the early 1980's. I guess my symptoms and my Scandinavian ancestry were suggestive enough of hemochromatosis that he wanted to check this out. [The disease seems to be most prevalent in Ireland and Norway].

My iron levels were well within normal range and that was the end of that.

Considering that ME seems to be pretty well known in Ireland and Norway, I'm guessing that if there were a connection between ME/CFS and hemochromatosis it would have already been found there.