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Reprinted from VirusMyth.Net, edited & updated for Alive &
Well May 2004
FALSE POSITIVE VIRAL LOADS
By Matt Irwin, MD
“In spite of the widespread use of viral load tests,
there are serious doubts about their accuracy. The most significant
is false positive viral loads in people with no risk factors for
HIV who test HIV negative. In the US, random screening using viral
load would produce 30 to 100 false positives for every 4 true positives…”
Abstract
Polymerase chain reaction (PCR) and other RNA assays are used with
increasing frequency in a variety of fields of science and medicine,
especially in the study of the human immunodeficiency virus (HIV)
and acquired immunodeficiency syndrome (AIDS). In spite of the widespread
use of these tests, however, there are several inconsistencies that
raise serious doubts about their accuracy. RNA assays are perhaps
most heavily relied upon in the medical management of people diagnosed
HIV-positive or with AIDS, where they are used to measure their
"viral load". Because many important clinical decisions
are made based on these tests, the highest standards of sensitivity
and specificity should be required.
The most significant inconsistency in RNA assays for people diagnosed
HIV-positive is the presence of false positive viral loads. False
positives occur commonly in 3% to 10% of people who have no risk
factors for HIV and who test negative on the HIV antibody tests.
In the United States where the prevalence of “HIV infection”
is officially estimated at 0.4%, this false positive rate means
that random screening using the viral load test would produce 30
to 100 false positives for every 4 true positives.
Other inconsistencies include the finding that between 99.99% and
99.9999% of the HIV virions estimated by viral load are not infectious,
which raises questions about disease causation in people who test
HIV positive. This paper will review a number of studies that focus
on false positive results on HIV RNA assays, and will also briefly
review some of the other inconsistencies that raise questions about
their accuracy. This is not meant to be a comprehensive review,
but rather to highlight the most serious problems and discuss their
implications for “management of HIV infection” as well
as other research. The most likely explanation for the findings
is that much of the RNA measured by viral load assays comes from
other microbes and from normal human cells.
Introduction
Monitoring of viral load is used in a variety of ways in people
diagnosed HIV-positive. It has become one of the primary methods,
along with measuring CD4+ T-lymphocyte counts, for making treatment
decisions such as starting or changing antiretroviral medications,
or for deciding the severity of “HIV-infection”. If
someone has been diagnosed HIV-positive, viral load can be used
to diagnose them with AIDS in the absence of any clinical symptoms.
It is rarely used to diagnose someone as being HIV-infected, however,
because of the high rate of false positives.
It is generally believed, especially among clinicians, that the
numbers generated by a viral load test represent active viruses
present in each milliliter of a person's blood, but this is not
correct. The viral load test is used to measure the quantity of
RNA fragments believed to be specific to HIV that are present in
each milliliter of a person's blood. Even this is not completely
accurate, however, because the quantitative measurement is done
indirectly using mathematical equations as opposed to any method
of direct counting. In actuality, the probes used to identify short
RNA sequences are assumed to come from HIV. Whatever is found by
the probes is then amplified exponentially by a string of replication
steps. Only after all of these amplifications are complete can the
RNA fragments be detected and counted. Then a complex mathematical
estimation is used to ascertain how many RNA fragments were present
in the original sample of blood, a calculation which finally generates
the number allegedly representing a person's "viral load".
Each one of these steps introduces the potential for inaccurate
results, from the assumption that only RNA from HIV will be identified
and amplified, to the assumption that the mathematical formula will
accurately reveal how many of such RNA copies were originally present.
False positives occur with all of the available RNA assays, including
the newer generation of viral load tests (Mendoza et al 1998). When
tests are done on the serum of people considered HIV-negative, 3%
to 10% commonly have positive viral loads. The highest reported
rate of false positive results is a remarkable 60% (HIV surrogate
marker coll. group 2000). Although most cases reported have false
viral loads of 10,000 or less, there have been reports of false
positive viral loads as high as 100,000 copies per milliliter. In
the United States, where the prevalence of HIV is estimated to be
1 in 250 people (0.4%), a false positive viral load rate of only
2% would still mean that random screening of the population would
result in 5 false positives for every true positive, while a false
positive rate of 10% would result in 25 false positives for every
one true positive. The most likely explanation for this high false
positive rate is that HIV-RNA assays commonly react with non-HIV
RNA, such as that produced by normal human cells and other microbes.
The human genome has about 3 billion base pairs of DNA, while that
of HIV has only about 10,000. Because of this difference, human
cells produce a great deal more RNA than HIV does. RNA from human
cells can be released in large quantities during times of rapid
cell death, which occurs during the infectious and inflammatory
processes of disease commonly present in people diagnosed HIV-positive.
This could greatly increase the potential for false positive viral
loads in the very population being studied. The high rate of false
positive results from HIV RNA assays suggests that some of the 3
billion base pairs in the human genome could be producing RNA that
is mistakenly attributed to HIV. This argument is strengthened by
the fact that typical RNA assays look for only about 3% of HIV's
genetic material, or about 300 out of 10,000 base pairs.
Another fact that increases the risk of false positive viral loads
is that these tests use RNA sequences that are based on the antibody
proteins detected by the ELISA and Western Blot antibody tests.
This means a person with a false positive or indeterminate result
on either of the antibody tests is also very likely to have a false
positive result on the viral load test. False positive and indeterminate
results on ELISA and Western Blot tests are well known. For instance,
20 to 40% of healthy blood donors with no risk factors for HIV infection
and who testnegative on the ELISA test will test indeterminate on
the Western Blot test (Proffitt et al. 1993).
As mentioned previously, studies have found that the number of viral
copies estimated by viral load tests represent between 99.99% and
99.9999% non-infectious viruses (Piatak et al 1993). Non-infectious
viruses are not able to cause disease, since by definition they
cannot infect cells. It is also possible that these "non-infectious
viruses" which may make up an entire viral load count are not
really viruses at all, but rather represent the detection of RNA
from non-HIV sources.
This paper will present some inconsistencies about viral load measurements
as well as explanations for the inconsistencies which suggest that
the tests are not accurately measuring HIV activity. Further, it
will review a number of studies documenting the relatively common
occurrence of false positive viral loads in people who are considered
HIV-negative. Some discussion of antiretroviral (anti-HIV) medications
is also included. Since anti-HIV medications interfere with RNA
and DNA synthesis in nearly all human cells as well as in other
non-HIV microbes, (Schmitz et al. 1994, Dalakas et al. 1994, Bacellar
et al 1994, Physician's Desk Reference/PDR 1999, Cassone 1999, Atzori
2000, PDR 1999) anti-HIV medications could reduce viral loads dramatically
even if the RNA detected by viral load tests comes from normal human
cells or other microbes present in the person being tested.
I. Viral loads represent 99.99% to 99.9999% non-infectious virus
Viruses can only cause damage if they are infectious because they
need to infect cells in order to cause cell death. Researchers attempting
to see what portion of the huge numbers of HIV reported by quantitative
PCR represent active, infectious viruses have found that as few
as 1 in 10 million (0.0001%) are actually infectious. A virus that
cannot infect another cell is essentially sterile; it cannot harm
any cells if it cannot infect them. Following are some comments
from a study published in Science in 1993 in which researchers found
that the vast majority of viral particles estimated by viral load
assays were non-infectious and non-culturable (Piatak et al. 1993).
"Circulating levels of plasma virus determined by [quantitative]
PCR correlated with, but exceeded by an average of 60,000-fold,
numbers of infectious HIV-1 that were determined by quantitative
culture of identical portions of plasma... Total virions have been
reported [in other studies] to exceed culturable infectious units
by factors of 10,000 to 10,000,000, ratios similar to those we observed
in plasma." (Piatak et al. 1993, page 1752)
This means that these researchers estimated that only about 1 in
60,000 virions found using quantitative PCR were actually infectious,
and that other studies have found as few as 1 in 10 million. The
researchers were not able to culture any virus at all in more than
half (35 of 66) of patients who tested HIV positive and had viral
loads. HIV positives with no infectious virus at all had viral loads
as high as 815,000 copies per milliliter. The study subjects had
all tested positive on the ELISA and Western Blot antibody tests,
the two tests currently used to diagnose people as being HIV-positive.
They all had high viral loads, yet the majority of them had no culturable
infectious units of HIV. This difficulty in finding active HIV particles
in HIV positives has been encountered by many other researchers
who have tried to confirm the presence of HIV in people's blood
(Chiodi 1988, Gallo 1984, Learmont 1992, Popovic 1984, Sarngadharan
1984, Schupbach 1984).
II. False positive viral loads
Studies examining false positive viral loads have found false positive
rates varying widely from 0 to 60%, with the most common rates being
about 3 to 10%. The numbers of viral copies per milliliter of blood
found in people considered HIV-negative have ranged from 48 to over
100,000. These levels are much higher than those used to make treatment
decisions in people diagnosed HIV-positive. Current recommendations
are that if a person is on antiretroviral combination therapy and
their viral load rises above undetectable, their medications should
be changed. For instance, a recent article on the use of viral load
in managing HIV-infection states:
"Failure to achieve the target level of 50 copies per milliliter
after 16 to 24 weeks of treatmentshould prompt consideration of
drug resistance, inadequate drug absorption, or poor compliance.
... For patients in whom a plasma viral load below detectable level
has been achieved, a general guideline is to change antiretroviral
drug therapy if the plasma HIV RNA concentration is found to be
increasing. Ideally, any confirmed detectable plasma HIV RNA is
an indication to change therapy. In some patients, it may be reasonable
to wait until there is a documented increase in the plasma HIV RNA
level to greater than 2000 to 5000 copies per milliliter."
(Mylonakis et al. 2001, page 483
This shows clearly that a level of only 50 copies per milliliter
is considered significant, and yet itwill be shown that false positive
viral loads up to 100,000 copies per milliliter have been detected
in HIV negatives. Learning about what conditions predispose a person
to have a false positive viral load would help a great deal in managing
someone diagnosed HIV-positive, since it could help in determining
how much of a person's viral load represents actual HIV activity.
Although it is reasonable that false positive viral loads would
appear any time there is a great deal of cell death because of the
high quantities of RNA that are released when cells die in large
numbers, no controlled studies attempt to determine what factors
influence the likelihood of false positive results.
IIa. False positive viral loads on three different viral load tests
In 1998 Mendoza et al. published a significant article on false
positive viral loads in which they compare results on three different
viral load assays (Mendoza et al. 1998). Several days after diagnosing
a 5 month-old child with HIV-infection based on a positive viral
load of 3044 copies per ml and starting him on antiretroviral medications,
they discovered that he and his parents all tested negative on the
antibody tests. After extensive follow-up testing of the child and
his parents, they concluded, "A suspicious false-positive viral
load result becomes the sole explanation for this controversy"
(page 2076). This event prompted them to perform a simple experiment
that they describe as follows:
"Since viral load tests were approved for quantification of
viremia in already known HIV-positive individuals, we were interested
to know their specificity. For this purpose, we selected 20 healthy
volunteers, all of whom yielded negative results for HIV antibodies
using different screening tests. Plasma from all of them were analyzed
by three currently available HIV viral load tests." (Mendoza
et al. 1998, page 2076)
The first assay, which used a branched DNA test from Chiron laboratory,
found that 2 of the 20 volunteers (10%) had a positive viral load,
one with a viral load of 10,620 copies per mL and one with 2,020.
A nucelic acid-based amplification test from Organon Teknika also
yielded 2 of 20 false positives, although with smaller values of
150 and 480 copies per mL. The final assay, RT-PCR Monitor from
Roche, was run in two different ways, once looking for only a particular
HIV subtype and once looking for any type of HIV. When looking only
for the subtype, only 1 of 20 (5%) was positive, but when looking
for any type of HIV, 4 of 20 (20%) were positive. Although this
rate of false positives (20%) was higher than that found for the
other tests, the values measured were lower, ranging from 48 to
253 copies per mL. The authors do not reveal whether the same people
who tested positive on one assay were more likely to test positive
on another, but they do state that repeat testing reproduced the
same results in more than half of the specimens that were able to
be retested.
This study is significant not only because it found false positives
in three different RNA assays, but also because it used healthy
volunteers with no risk factors for HIV infection, in whom the possibility
of HIV infection is exceedingly small. Most of the other studies
to be reviewed only looked at the accuracy of the RT-PCR Monitor
RNA assay in measuring viral loads, and they often studied people
with known risk factors or known exposures to HIV.
IIb. False positives of 100,000 copies per ml
In 1997, a study was published with a carefully documented false
positive viral load of up to 100,000 copies per ml (Schwartz et
al. 1997). The patient in question was a participant in an HIV vaccine
clinical trial who was being closely followed and whose blood had
been tested for antibodies to HIV every few months for several years.
A viral load test was first performed on his serum when the patient
reported flu-like symptoms. Flu-like symptoms are thought to suggest
the onset of acute infection with HIV, which is also called "acute
retroviral syndrome". The viral load test was positive, and
the authors decided to run viral load tests on all of the available
samples of blood from that patient which had been stored over the
course of the clinical trial. The antibody tests on these serum
samples had all been negative, but they now found that four of the
samples from several years prior had positive viral loads with the
largest viral load being "in the range of 10,000 to 100,000".
This patient had repeated testing for the next year, which continued
to show negative results, meaning the likelihood he actually was
experiencing acute HIV infection is extremely low.
While such a large false positive result is unusual, even one such
result is significant enough toquestion the practice of choosing
an arbitrary cutoff above which a viral load test is thought to
accurately diagnose HIV infection. A cutoff that is commonly used
is 10,000 copies per ml. For example, a very recent study that looked
retrospectively at blood samples from people with risk factors for
HIV infection and flu-like symptoms assumed that viral loads above
10,000 represented "true positives" while those below
10,000 did not (Daar et al. 2001). This experiment was described
by Daar et al. as follows:
"Follow-up was not available for these 127 patients [cohort
1]; therefore, before testing any samples, we determined that an
HIV RNA result above 10,000 copies/mL would be considered a true-positive
result. ... Two of 127 patients in cohort 1 were negative for HIV
antibody and negative for p24 antigen, but positive for HIV RNA
with levels of greater than 100,000 copies/mL. For the purpose of
this analysis, they were considered to be true positive for primary
HIV infection." (Daar et al. 2001, page 26)
While it is possible that these patients would have eventually had
positive HIV antibody tests, itappears inappropriate to assume that
this is the case in light of the studies previously mentioned describing
the high rate of false positive results. These two patients may
represent more examples of false positive viral loads over 100,000,
but it is impossible to be sure without further follow up data.
This study by Daar et al. also looked at two other cohorts of people
at risk for HIV infection. In the cohorts, follow up antibody testing
was available and they found that 8 of 217 (3.7%) subjects had a
false positive result with viral loads ranging from 50 to 2000 copies/ml.
Because the authors include cohort 1 in their data even though no
follow-up data is available for this cohort, their conclusions and
abstract report a lower false positive rate of 2.6%.
Although not the primary subject of this paper, the accuracy of
p24 antigen testing was also called into question by Daar et al
(2001). Some people in the study cohorts were found to be positive
for HIV antibodies on initial screening, and were described as having
"chronic HIV-infection". The large majority of these people
(82%) were negative for p24 antigen, a protein thought to be a specific
and integral part of the virus. People with viral loads at least
as high as 631,000 copies/mL were still negative for p24 antigen,
which again raises the question of how much virus was really present
in these people.
Another recent study by Rosenberg et al. (1999) also found very
high viral loads in people who were negative on the HIV antibody
tests, with the highest being greater than 1.5 million copies per
ml. This study was designed as an attempt to see if people diagnosed
previously with acute mononucleosis were actually having symptoms
of acute HIV-infection. They used a single stored blood sample with
no clinical outcome or follow-up. The authors found 4 of 563 (0.7%)
subjects had positive viral loads with negative ELISA antibody tests.
This rate of 0.7% is much smaller than the false positive rates
mentioned previously, which actually increases the probability that
they were false positives, although no follow-up clinical data or
testing was available. As with the authors just described, they
assume that the people in their study are HIV-positive based solely
on the viral load tests. While it is again possible that these people
were newly infected with HIV, it is also possible that they represent
yet another example of false positives, this time with viral loads
of over 1.5 million.
IIc. A meta-analysis of RNA assay false positive results
In 1996 Owens et al. published a meta-analysis of 96 different studies
that looked at the specificity and sensitivity of the polymerase
chain reaction (PCR) in diagnosing HIV infection (HIV surrogate
marker coll. group 2000). They found that the specificity of PCR
varied widely in these studies from a low of 40% to a high of 100%,
which means that false positive rates varied from 60% to 0%. They
would have found even higher false positive rates if they included
"indeterminate" PCR results as being positive. In the
studies of highest quality, according to the authors, the false
positive rate ranged from 5% to 0%. The authors also found that
studies using more recent PCR technology were no more accurate than
older studies, and that publication bias may have prevented studies
with worse results from being published. Here are their descriptions
of these findings:
"Our subgroup analysis shows that studies published only as
abstracts provided lower estimates of the sensitivity and specificity
of PCR. This may indicate publication bias-the preference for publishing
favorable rather than unfavorable studies. ... We did not find evidence
that performance of PCR improved over time." (HIV surrogate
marker coll. group 2000, page 810)
They also discuss a common factor that can lead to claims of falsely
high specificities. This comes about because the PCR test result
is called positive or negative based on a threshold value, and is
not a "yes or no" result. If the threshold is chosen so
that even a very mild reactivity is interpreted as positive, then
many people who are not actually positive will be mistakenly identified.
If a high threshold is required and only very strongly reactive
samples are counted, then specificity will increase, but more people
who are actually positive will be missed resulting in poor sensitivity.
As described by the authors:
"Because both sensitivity and specificity are determined by
the choice of the threshold for an abnormal test result, there is
an inherent tradeoff between them. The threshold can be chosen so
that PCR is 100% sensitive or so that it is 100% specific, but not
normally both. Thus, a study that only evaluates the sensitivity
of PCR or only evaluates the specificity of PCR provides insufficient
information for evaluation of the test's performance." (HIV
surrogate marker coll. group 2000, page 812)
IId. False positive viral loads: A case series
Rich et al. (1999) published a case series describing three patients
with false positive viral loads. While the authors do not give information
that would allow an estimation of the rate of false positives, their
series is significant because it demonstrates that false positives
on viral load may be likely to occur in conjunction with false positives
on both the ELISA and Western Blot HIV antibody tests. Since the
RNA assays look for RNA that is based in the amino-acid sequence
of the same proteins used in the ELISA and Western Blot, this would
not be surprising. The ELISA is used as a screening test and the
Western Blot, which separates the same proteins that are used in
the ELISA into 10 separate bands, is used as a confirmatory test.
The Western Blot test is only done if the ELISA is positive. While
the first two cases had negative antibody tests, the third case
had a positive ELISA and an indeterminate Western Blot test. This
case was a 20 year-old healthy woman whose test results were described
as follows:
"During a four month period after her indeterminate result
on the Western Blot test, she had positive results on ELISA and
indeterminate results on Western Blot on separate occasions. Five
months later, both the ELISA and Western Blot tests yielded negative
results, but the patient had a plasma viral load of 1300 copies/ml."
(Rich et al. 1999, page 38).
The possibility that false positive viral load tests are more likely
when false positive ELISA or indeterminate Western Blot tests occur
is reasonable, and furthur studies would not be difficult. Western
Blot tests are indeterminate in 20 to 40% of healthy blood donors
who are negative on the ELISA test (Proffitt et al 1993). While
this extremely high indeterminate rate raises questions about the
most heavily relied upon confirmatory test, it would make future
research easy to perform because of the plentiful supply of people
with indeterminate tests in whom viral loads can be measured.
IIe. False positive viral loads after needle sticks with HIV positive
blood
Gerberding et al. (1994) conducted a study of HIV contaminated needle
sticks, and in the process also uncovered data that call into question
the value of viral load/PCR testing. They performed PCR tests on
133 of the 327 healthy workers who had experienced needle sticks
in their clinic. All 133 subjects remained HIV negative on the ELISA
antibody test, but seven had "indeterminate" PCR results,
while four others had one or more actual positive results, making
a false positive rate of 3%. If the indeterminate results are counted
as well, the false positive rate is 8%. Gerberding et al. comment
on their findings with PCR as follows:
"The failure to demonstrate seroconversion... among those with
positive PCR tests suggests that false positives occur even under
stringent test conditions. The low predictive value of a positive
or indeterminate PCR test... contraindicates the routine use of
gene amplification in this clinical setting." (Gerberding et
al. 1994, page 1415)
IIf. False positive tests for HIV-DNA
Another assay that was once heavily promoted is an HIV-DNA assay,
which is similar to an HIV-RNA assay and uses the same polymerase
chain reaction (PCR) technology. A study looking at this assay was
published in 1992 by Busch et al.. They conducted PCR-DNA tests
on 151 ELISA-negative people and found that 18.5% (28 people) had
positive PCRs. Furthermore, they found that only 25.5% of people
diagnosed HIV-positive had positive PCR's. Their conclusion draws
attention to how close the two numbers, 18.5% and 25.5%, are:
"This study of PCR detection of HIV-DNA in serum identified
a disturbingly high rate of nonspecific positivity with a widely
employed gag primer pair system [“gag” is a protein
considered to be specific to HIV]. In fact, the overall positivity
was not significantly different for serum specimens from seropositive
patients and seronegative control donors (25.5% vs 18.5%). ... In
contrast to the high rate of false positive results observed with
gag primers, env DNA [“env” is another protein thought
to be specific to HIV] was not detected by laboratory B in any of
the specimens from either seronegative or seropositive individuals.
Absence of reactions with both primer pairs from all 59 specimens
from seropositive persons meant that no serum sample could be confirmed
positive for HIV-DNA, i.e. 0% sensitivity. This finding is in marked
contrast to the high sensitivity reported previously by Laboratory
B for both gag and env primers." (Busch et al. 1992, pages
874-875).
Although HIV-DNA testing is not used for viral load measurements,
it is of interest to note the significant problems that developed
with this test even though the laboratories that produced it claimed
that it was highly accurate, sensitive, and specific. The fact that
they found 0% sensitivity for one of the key proteins thought to
be specific to HIV again suggests that these assays are mostly reacting
with non-HIV DNA and RNA and mistakenly attributing it to HIV.
III. Alternative explanations for variations in viral loads and
improved clinical outcomes
IIIa. The placebo effect
Although people whose viral loads are reduced successfully by antiretroviral
drugs do have better clinical outcomes (Gilbert et al 2001), there
are several other possible explanations for this besides the widely
accepted belief that reduced viral loads represent reduced HIV activity
and reduced numbers of HIV particles which therefore result in improved
clinical health.
One factor ignored by this model is the placebo effect. Although
many HIV drug trials are double blind trials, viral load scores
are not blinded. Because viral loads are commonly thought to represent
the number of viruses per milliliter of blood, it can be terrifying
to hear that one's viral load is in the thousands, hundreds of thousands,
or even in the millions. Receiving news of a dramatically lowered
viral load can have a direct effect on a person's physiology, even
if the RNA being measured is not coming from HIV. Hearing that one's
viral load has been dramatically lowered can reduce the emotional
and psychological stress, anxietyand depression, which can be severe
in those diagnosed HIV-positive. Improved psychological and emotional
well-being may promote various health-enhancing behaviors such as
exercise, good nutrition, improved medical care and self regard.
There is also good reason to believe that the patients in the clinical
trials in question can see through the double blind. It has been
shown that most participants in drug studies can correctly guess
whether they are getting active or placebo medications (Greenberg
and Fisher 1997). There are several reasonable explanations for
this finding. In the case of HIV, one is that the viral load is
often reduced more by more medications. (HIV drug trials do not
use true placebo controls. Rather, they test a patient’s current
therapy against current therapy plus the new drug being tested.)
Another explanation is that groups receiving the additional medications
also have significantly more side effects.
IIIb. RNA reductions in normal human cells and other microbes
The number of viruses estimated by viral load tests is based on
measurements of RNA fragments so that any change in overall RNA
levels in the blood could potentially alter a person's viral load,
even if this RNA does not come from HIV. Many antiretroviral drugs
have a short-term antimicrobial effect, which can result in a temporary
improvement in health by directly inhibiting RNA and DNA synthesis.
These drugs also cause reduced RNA and DNA synthesis in a wide variety
of human cells including red blood cells, white blood cells, nerve
cells, bone building cells, and muscle cells which result in some
of their most common adverse effects as reported in clinical trials
(Schmitz et al. 1994, Dalakas et al. 1994, Bacellar et al 1994,
Physician's Desk Reference/PDR 1999). Non-HIV microbes found to
be suppressed by these drugs include Pneumocystis carinii, Candida
albicans, Enterobacter, Shigella, Salmonella, Klebsiella, Citrobacter,
and E-coli; many other microbes not yet studied may also be affected
(Cassone 1999, Atzori 2000, PDR 1999). The reduced RNA and DNA synthesis
in the microbes will result in reduced infection, while in human
cells it will result in reduced activity, reduced cell division,
and reduced inflammatory response to infection. This reduced infection
and inflammation, as well as the direct suppression of RNA production,
is likely to result in dramatic reductions of RNA levels in the
blood stream. If viral load assays commonly measure RNA from normal
human cells and other microbes and mistakenly attribute it to HIV
as is suggested by the articles reviewed in this paper, then the
reduced RNA and DNA synthesis they cause could obviously result
in a lowered viral load, even if there is no HIV present.
Unfortunately, the antimicrobial effect of antiretroviral drugs
is short lived as microbial resistance develops quickly (PDR 1999).
This may be another explanation for people whose viral loads increase
while taking anti-HIV drugs since microbes and human cells could
adapt and increase their RNA production in spite of the presence
of anti-HIV medications. Other drugs that interfere with RNA synthesis,
such as many cancer chemotherapeutic agents, would also cause viral
loads to fall dramatically, even in a person who is HIV-negative.
Another possibility raised by these arguments is that the rebound
in viral loads that is often seen soon after a person stops taking
antiretroviral drugs may not represent renewed HIV activity as is
commonly thought. When human or microbial RNA and DNA production
is suppressed by artificial means, the cells will naturally try
to compensate by increasing their production of RNA and DNA. When
the inhibiting effect of the drug is removed, this accelerated production
may become dominant and cause a rapid increase in viral load even
if HIV is not present.
IIIc. Large reductions in viral load are no better than small reductions
Comparisons of studies showing positive effects from lowered viral
loads reveal another inconsistency: dramatic reductions in viral
load do not offer better clinical benefit than small reductions.
An analysis of all 16 randomized trials that compared outcomes based
on drug-induced lowering of viral load found that drugs that cause
marked lowering of viral loads do not confer better clinical results
than only mild reductions, and drugs that cause similar reductions
in viral loads have widely varying clinical outcomes (HIV Surrogate
Marker Collaborative Group 2000). If a surrogate marker such as
viral load is a reliable indicator of drug efficacy, then more dramatic
reductions in viral load should result in better clinical outcomes,
but this is not the case. Here are some quotes from the authors
of a study examining this question (published in AIDS Research and
Human Retroviruses in 2000):
"If a prognostic marker is reliable as a surrogate endpoint,
then comparisons of randomized treatments that show large differences
in marker levels should also show large differences in the hazard
of AIDS/death. ... [In our analysis], trials that show similar differences
in marker effects may have quite varied differences in clinical
outcome." (HIV Surrogate Marker Collaborative Group 2000, pages
1129-1130)
In their abstract they state simply:
"Short-term changes in these markers [HIV-1 RNA and CD4 count]
are imperfect as surrogate end points for long-term clinical outcome
because two randomized treatment comparisons may show similar differences
between treatments in marker changes but not similar differences
in progression to AIDS/death." (HIV Surrogate Marker Collaborative
Group 2000, page 1123)
IIId. Alternative explanations for reduced AIDS death rates
Although antiretroviral combination medical regimens are credited
with the dramatic reduction in AIDS death rates in the United States,
alternative explanations for the reduced rates are often overlooked.
The first problem is that the reduction in deaths began before the
new drugs were introduced. In 1995, the AIDS death rates began to
drop (CDC 1997), but the first protease inhibitor was not approved
by the FDA until December of 1995. In 1996, only 20% of people diagnosed
HIV-positive were taking the new medications, which is not enough
to account for the large drops in mortality that occurred (McNaughten
et al. 2001).
An alternative explanation for the reduction in deaths starting
in 1995 is that the number of new AIDS cases began declining in
1993 (CDC 1997). The drop in AIDS deaths that occurred two years
later in 1995 would be a logical extension of the decreases in new
AIDS cases. Additionally, in 1993 an expanded definition of AIDS
was introduced that allowed people with no clinical illness to be
diagnosed with AIDS—people with CD4 counts below 200 but no
clinical symptoms. Clinically healthy individuals represent about
half of all AIDS diagnoses since 1993. This means that most people
diagnosed since 1993 were not sick, whereas people diagnosed before
1993 were suffering from actual illness. The expanded definition
also makes it possible to diagnose more people with AIDS. Despite
this loosening of the diagnostic criteria, the incidence of new
AIDS cases began to fall in 1993, suggesting that the number of
AIDS cases would have dropped more steeply if this new definition
had not been introduced.
Conclusions
While this paper does not explain the cause of false positive viral
loads, it does demonstrate that there is a surprisingly high rate
of false positives. This finding raises enough questions to advise
caution regarding the current heavy reliance on viral load testing
as a basis for treatment decisions for people diagnosed HIV-positive.
False positive viral loads occur commonly in 3 to 10% of people
who are HIV negative, with the highest reported rate being 60%.
The highest false positive viral load reported was in the range
of 10,000 to 100,000 copies per milliliter, and it is possible that
some values over 1.5 million also indicated false positives although
no follow up data is available for these cases. This fact must be
contrasted with the current practice of initiating antiviral therapy
based on viral load or changing antiretroviral regimens if a person's
viral load does not fall below 50, as described in Mylonakis et
al.'s (2001) description of current practice guidelines.
One hypothesis that could explain these false positives is that
HIV viral load assays commonly misidentify RNA from normal human
cells and from other microbes as being from HIV. This hypothesis
could be tested by measuring viral loads in acutely ill people with
high RNA levels in their blood. Because anti-HIV medications reduce
RNA synthesis in a wide variety of cells, the reductions in viral
load that accompany the use of these medications may indicate a
non-specific reduction in total RNA burden, as opposed to a specific
reduction in HIV RNA. This argument is supported by the finding
of Piatak et al (1993) and others that most people with high viral
loads do not have culturable/infectious virus, and that even in
people who do have culturable virus, between 99.99% and 99.9999%
of the viruses are non-culturable and non-infectious. These "non-infectious"
viruses may represent falsely elevated viral loads due to misidentification
of RNA from human cells and other microbes.
Another implication of the findings outlined here is that the diagnosis
of HIV-infection continues to rely heavily on the ELISA and Western
Blot antibody tests. The accuracy of these antibody tests and the
experimental methodology used to determine their sensitivity and
specificity should thus be carefully examined, especially since
some authors consider false positives to be a problem with these
tests as well (Proffitt et al. 1993, Challakeree et al. 1997, de
Harven 1998a&b, Giraldo 1998, MacKenzie 1992, Papadopulos-Eleopulos
et al. 1993, Sayre et al. 1996). A strong correlation between positive
viral loads and positive HIV antibody tests is expected because
the viral load tests are designed to look for RNA sequences that
come from the proteins used in the antibody tests. If a person tests
positive for the antibodies to these proteins, they are likely to
have RNA with the same code sequences in their blood because this
RNA is used by the cells to code for these proteins. This means
that a false positive HIV-antibody test is very likely to increase
the risk of a false positive viral load.
Further examination of what factors increase the risk of false positive
or falsely elevated viral loads would be extremely valuable since
many treatment decisions are currently based on viral load measurements.
Until such research is undertaken, however, it is advisable to make
treatment decisions based on a person's symptoms and on the presence
of clinical illness, and not to rely heavily on viral load test
results. If a person appears to be clinically worse even though
their viral load has gone down, it may be advisable to reduce or
stop the medications being administered. Much of the reduced viral
load actually may be due to toxic effects on human cells. Likewise,
if a person is clinically healthy even though their viral load is
high and they are not on any anti-HIV medications, it may be advisable
to withhold medication and instead encourage conservative health
promoting measures that focus on nutritional, social, psychological,
and spiritual health, rather than focusing on toxic treatments whose
primary goal is to reduce the person's "viral load".
While attending medical school at George Washington University,
Matt Irwin, MD wrote several literature reviews on HIV and AIDS.
In addition to his degree in medicine, he holds a Master's degree
in social work from the Catholic University of America. Dr. Irwin
uses nutritional, psychological, social and spiritual interventions
as well as classical homeopathy in his practice of family medicine
near Washington, D.C.
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