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Presentation on CDC research program from 1999 to CFSCC

Discussion in 'BioMedical ME/CFS Research' started by Tom Kindlon, Dec 11, 2017.

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  1. Tom Kindlon

    Tom Kindlon Senior Member (Voting Rights)

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    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)
     
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  2. Tom Kindlon

    Tom Kindlon Senior Member (Voting Rights)

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    (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 --
     
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  3. Snow Leopard

    Snow Leopard Senior Member (Voting Rights)

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

    MeSci Senior Member (Voting Rights)

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    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.
     
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  5. Tom Kindlon

    Tom Kindlon Senior Member (Voting Rights)

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


     
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  6. Tom Kindlon

    Tom Kindlon Senior Member (Voting Rights)

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

    MeSci Senior Member (Voting Rights)

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    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!
     
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  8. Forbin

    Forbin Senior Member (Voting Rights)

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    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.
     
    Last edited: Dec 18, 2017
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