Blood Levels of Mercury Are Related to Diagnosis of Autism: A ReAnalysis of an Inportant Data Set

Posted on Jul 12, 2008 in Autism, Chronic Disease, Heavy Metals

Desoto MC, Hitlan RT.

Department of Psychology, University of Northern Iowa, Cedar Falls, Iowa 50614, USA.

The question of what is leading to the apparent increase in autism is of great importance. Like the link between aspirin and heart attack, even a small effect can have major health implications. If there is any link between autism and mercury, it is absolutely crucial that the first reports of the question are not falsely stating that no link occurs. We have reanalyzed the data set originally reported by Ip et al. in 2004 and have found that the original p value was in error and that a significant relation does exist between the blood levels of mercury and diagnosis of an autism spectrum disorder. Moreover, the hair sample analysis results offer some support for the idea that persons with autism may be less efficient and more variable at eliminating mercury from the blood.

PMID: 18006963 [PubMed – indexed for MEDLINE]

Journal of Child Neurology
Volume 22 Number 11
November 2007 1308-1311
© 2007 Sage Publications
hosted at

Blood Levels of Mercury Are Related to
Diagnosis of Autism: A Reanalysis of
an Important Data Set

M. Catherine DeSoto, PhD, and Robert T. Hitlan, PhD

The question of what is leading to the apparent increase in autism is of great importance. Like the link between aspirin and heart attack, even a small effect can have major health implications. If there is any link between autism and mercury, it is absolutely crucial that the first reports of the question are not falsely stating that no link occurs. We have reanalyzed the data set originally reported by Ip et al. in 2004 and have found that the original p value was in error and that a significant relation does exist between the blood levels of mercury and diagnosis of an autism spectrum disorder. Moreover, the hair sample analysis results offer some support for the idea that persons with autism may be less efficient and more variable at eliminating mercury from the blood. Keywords: autism; mercury; environmental health; neurotoxin; neurodevelopment; blood

There is a marked increase in the diagnosis of autism. The question of what is (and is not) related to this increase is crucial to millions of persons affected by the disorder. This article reanalyzes an original data set regarding the relation between blood levels of mercury and diagnosis of an autism spectrum disorder (ASD) by Ip et al. based on our finding of discrepancies in the original article.1 A review of what is known about the neurotoxic effects of mercury is beyond the scope of this paper,2 but the observable symptoms of acute mercury poisoning have been reported to match up with many of the problems observed in autism.3 Furthermore, mercury poisoning has sometimes been presumptively diagnosed as autism of unknown etiology until the mercury poisoning has been uncovered.4 Because there has been a several-fold increase in environmental mercury exposure, the hypothesis that the rise in autism could be related to an environmental increase in mercury levels is a reasonable one to pursue. Autism may result from a combination of genetic susceptibility (perhaps in the form of reduced ability to remove mercury or other neurotoxins from the system) and environmental exposure at key times in development.5-7 This would mean a generalized increase in mercury levels would be expected to co-occur with a generalized increase in autism, but some people exposed to relatively high mercury would not be affected if, for example, their bodies were very efficient eliminators of such toxins. Only if an exposed infant or fetus also had a genetic susceptibility that makes one less able to remove mercury (or other heavy metals) would normal levels of mercury exposure lead to problems. Alternatively, it could be that genes that help detoxify get switched on and start to express themselves a little later than normal in those genetically predisposed to autism; or perhaps, autism results from some combination of these theories. Nevertheless, if mercury does play any causal role in facilitating a diagnosis of autism, there would likely be at

least some relation between high mercury measured in the blood and symptoms of autism even if ability to metabolize mediates the relationship between exposure and neural toxicity. This is because even if exposure is identical, those who remove mercury less effectively should still have higher levels in the blood. Interestingly, results of hair samples could be expected to be somewhat mixed. The level of mercury in hair may be better understood as an indication of how much mercury has been removed by the body as opposed to the level in the body.6 If people are approximately equal in their ability to remove circulating mercury from the bloodstream, then these 2 indicators should match up closely, but if a person’s ability to excrete is low, their hair samples might not be elevated even when their blood levels are high. Fido and Al-Saad found that mercury levels in hair samples were higher in children diagnosed with autism.8 These children were aged 4 to 7. In contrast, Kern et al. reported that mercury hair levels were not significantly different, but were lower at a marginally significant level.9 Kern et al. used younger children, ages 1 to 6. Holmes et al. performed the most direct test of the hypothesis that autistic children may be deficient in terms of ability to remove mercury from circulation. 6 This study estimated mercury exposure of the mothers via a mercury exposure survey questionnaire. They then analyzed the first haircuts of the autistic children and a group of controls (the first haircuts would reflect mercury excretion in utero and very early life). In the autistic group, severity of autism was inversely related to hair mercury levels. This means that the more severe autistic cases actually had less excretion of mercury. Furthermore, among the normal children, hair levels of mercury were correlated to the mother’s mercury exposure (as would of course be expected). But among the autistic children, there was no linear relation between the mother’s mercury exposure and excretion of mercury in the hair. As the authors state, this pattern of results is easily understood if one considers “detoxification capacity of a subset of infants,”6 (p 6) such that the bodies of those diagnosed with autism appeared to be less able to excrete and/or metabolize the mercury they were exposed to. As the rise in autism is relatively recent, it is not surprising that research into the etiology has not kept pace. Indeed, there are few published articles that consider blood levels of children with mercury that utilize a control group; a psycInfo search using the words “autism,” “mercury,” and “blood” yields only one hit.1 Given the high stakes involved, it is crucial that early reports of the connection between blood mercury levels and autism not be misstated. Even a small effect size would be of great theoretical and practical consequence. In 2004, Ip et al. reported that no relationship existed between mercury blood levels and diagnosis of autistic spectrum disorder among a group of children with an average age of approximately 7 years. While attempting to estimate the effect size based on the Ip et al. statistics, we realized that the numbers reported by Ip et al could not be correct. The means and standard deviations reported in the 2004 article yielded an easily significant t value (autism mean = 19.53 nmol/L, SD = 5.6, n = 82; control mean = 17.68 nmol/L, SD = 2.48, n = 55 gives a t = 2.283, two-tailed P = .024 or one-tailed P = .012). Ip et al. wrote that the P value was “(P) = .15,” 1(p432) and that their data indicate “there is no causal relationship between mercury and as an environmental neurotoxin and autism.”1(p431) After the error was brought to the attention of the authors, a new analysis was conducted by the original authors and they found the original t test to be in error and the P value to be a mistake (refer to Erratum, p.1324). Based on their corrected analysis, the authors report the revised P value for their t test to actually be P = .056. We disagree on several grounds that these data indicate no significant effect exists, and report on a completely new reanalysis of the original data set.Methods Outliers were removed prior to statistical analysis. An outlier is defined as a score that is “substantially greater or less than the values obtained from any other individual.” 10(p521) Outliers have an unduly large influence on the outcome of a statistical test. What actually qualifies as an outlier differs depending on the research question and the statistician analyzing the results; however, values greater than 3 standard deviations either above or below the mean generally qualify as extreme cases.11 Within the Ip et al. data, there were 2 such values that were not removed prior to our reanalysis. These 2 values were more than 3 standard deviations above the mean, and both of these values were far from any other score. (Other scores were within 3 points of the next individual; these 2 scores were each 15 or more points away from any other score in the distribution.) To avoid the appearance that these 2 outliers were removed to influence the statistical outcome as opposed to objective criteria for cleaning a data set, it should be noted that the biggest outlier of the 2 was an unusually high blood mercury level of 98, which was in the autistic group. To be clear⎯if anything, removal of the outliers resulted in a more conservative test as it actually decreased the mean difference between the 2 groups.

Logistic regression was performed using blood mercury
level as the predictor and the autistic/control group as the
criterion. Results of this reanalysis indicate that blood
mercury level can be used to predict autism diagnosis.
Data included: r = .20, r2 = .04, F(1,133) = 5.76, P = .017.
This finding indicates that there is a statistically significant
relationship between mercury levels in the blood and
diagnosis of an autism spectrum disorder.
There was no difference in the mean hair levels where
t(135) = .24 and one-tailed P = .40; this is essentially the
same result reported in the original article. However,
given that hair levels would normally be expected to be
highly correlated to blood levels, it might be surprising
that blood levels could predict an autism spectrum diagnosis,
but that hair mercury levels could not. Indeed, hair
and mercury levels for the full sample were correlated
(r = .86, P < .001) indicating that about 75% of the variance
in hair levels was accounted for by the mercury level
in the blood. To us, the question turned to what the other
25% of the variance might be due, and whether the
assumptions of the t test were violated. Although not the
central focus of this report, these results could certainly
help to inform future researchers of the nature of the
relation between autism and mercury, and we include this
information for completeness.
Exploratory Analysis. If one hypothesizes that persons
with autism are less able to excrete mercury, especially
when their blood levels get in the higher range, one might
expect that the correlation between blood and hair levels
would break down at the higher blood levels among the
autism spectrum group (a type of heteroscedasticity).5

Another way of looking at it, the relationship between blood
level and hair excretion may be different for persons with
autism than those without autism. Levine’s test of equality
of variance indicated the variance in hair mercury was not
evenly distributed between the autism and control groups (F =
5.98, P = .017). We calculated the correlation for persons
whose circulating levels of mercury were in the top quartile
separately for the autism and control groups. The correlation
between blood and hair levels of mercury was r = .91
for the control group (accounting for 84% of the variance).
For the autistic group, the correlation was r = .73, meaning
only about 55% of the variance in the hair mercury levels
was attributable to the blood mercury level differences.
To check the hypothesis that hair excretion was overall
lower than would otherwise be predicted based on a
certain blood level in the autistic group, a best fit regression
line was calculated (y = 10.3, x = ‚àí2.48) indicting
that for each unit increase in hair level, blood level
increased by 10.3 units. A t test on the residuals showed
that autistic participants were significantly more likely to
have lower hair mercury levels than would be predicted as
a function of their blood levels, t(133) = ‚àí2.92, P < .005;
see Figure 1). It should also be noted that the presence of
unequal variances or nonrandom residuals (in this case,
autistic persons are both more likely to have greater variability
at high levels of circulating mercury and a lower
hair value for a given blood level) are both violations of
important assumptions of the t test; a t test of hair mercury
is therefore probably not a valid means to predict
autism diagnosis as a function of mercury exposure. We
performed an analysis of covariance (ANCOVA) with
autism diagnosis as the independent variable and hair
mercury level as the dependent predictor using blood levels
as a covariate. Results indicate that hair level may be
related to diagnosis of autism, not as a predictor in terms
of absolute value, but such that for equivalent circulating
levels of mercury in the body, those with ASD excreted
less than normal such that F(1,134) = 3.9 and P = .05. To
sum, the relationship between blood levels of mercury
and mercury excreted in the hair is reduced for those with
autism compared with nonautistic persons; furthermore,
the difference between autistic and nonautistic persons is
most pronounced at high levels of mercury.
In statistics, obtaining a probability value of P < .05 indicates
that the obtained test statistic (based on one’s sample)
is extremely unlikely (less then 5% chance) to have
been obtained by chance alone. By convention, this value
is usually set at .05 (as a balance of type 1 and type 2
errors); however, this value is, in fact, arbitrary and statistical
probability tables for hypothesis testing always
include a range of probability values⎯not only probability
at the .05 level. Given that this is the first direct test
of this hypothesis and considering the potential importance
of finding a relation between mercury blood levels
and autism, it is just as important to avoid a false negative
as a false positive. As the original authors have now currently
calculated, the obtained difference suggests that
there is probably a real difference (specifically that the
chance that a real effect exists is about 94%, or, conversely,
that the chance the null effect is true is less than 6%, which

misses the conventional .05⎯or 5%⎯mark of
statistical significance). Given the close value to conventional
significance, most researchers would not call this a
firm rejection of the hypothesis, but might say it was marginally
significant. Most researchers facing a P value of
.056 would not want to categorically state that results
“indicate that there is no casual relation between mercury
level . . . and autism.”1 It concerns us that the original
authors would want to let this conclusion stand in light of
the new P value (which differs markedly from the .15 previously
reported in 2004).
Another issue to consider is the question of a one-tailed
or a two-tailed hypothesis test. Usually, researchers use a
two-tailed test, which tests if there is a “difference” between
2 groups. However, when the literature leads a researcher to
propose a specific direction of the difference, a one-tailed
test is called for, “Often a researcher begins an experiment
with a specific prediction about the treatment effect. For
example, a special training program is expected to increase
student performance, or alcohol consumption is expected to
slow reaction times . . . The result is a directional test, or
what is commonly called a one-tailed test.”10(p246)
Whether to use a one-tailed test or a two-tailed test
can be decided based on considering what would happen
if the results ended up in the opposite direction of what
one suspects. In this case, it would mean that the blood
mercury levels were lower in the autistic group. Would
this support the original hypothesis? (No!) However, if
this were to happen, that is, if the autistic group were significantly
lower in their blood mercury levels than the
normal group, the researchers would find themselves in
the incongruous position of having to accept their
hypothesis that autism is related to elevated levels of
mercury in the blood! The key point here is that their
hypothesis was directional, and a one-tailed test should
have been used. In this case, the just missed significance
of their new analysis using a two-tailed t-test (P = .056)
would have reached a conventional level of statistical significance
(with P < .03).
Although the statistics can be tedious, the bottom line is
that only by an apparent error in the original data analysis
was the original lack of effect found. The authors’ revised
calculation (t test) still has problems (two-tailed test for a
directional hypothesis, not removing clear outliers). And
finally, the willingness to characterize a t test with a .056
level of statistical significance as no effect is questionable,
especially in this particular case.
Of utmost importance (which outweighs the discomfort
of writing about an error made by colleagues whom we
know are generally competent researchers) is that potential
researchers who are trying to understand what is and is
not behind the rise in autism are not misled by even the
slightest misinformation. It is imperative that researchers,
medical professionals, and the public at large have the full
set of information. To put it in perspective, the connection
between taking aspirin and prevention of heart attack has an
effect size equal to .038 which represents an effect size
approximately equal to what we find between circulating
levels and ASD diagnosis in this age group.12 Just as important
is the fact that for those physicians in the aspirin group
who did have a heart attack, the heart attack was less likely
to be fatal. The effect size for this latter effect was .08 and
did not represent a significant difference from the placebo
group by traditional dichotomous significance testing.13 Yet,
this does not mean no effect exists or that the effect is not of
practical importance. We would encourage all researchers to
not only report whether a test of mercury and autism reaches
significance with the sample size used, but to report the
exact statistic and also effect sizes to help future researchers
resolve all the factors involved in the etiology of autism.

1. Ip P, Wong V, Ho M, Lee J, Wong W. Mercury exposure in children
with autistic spectrum disorder. J Child Neuro. 2004;19:431-434.
2. National Academy of Sciences. Toxicological Effects of Methylmercury.
Washington, DC: National Academy Press; 2000.
3. Bernard S, Enayati A, Redwood L, et al. Autism: a novel form
of mercury poisoning. Med Hypoth. 2001;56:462-471.
4. Chrysochoou C, Rutishauser C, Rauber-Luthy C, et al. An 11-
month-old boy with psychomotor-regression and auto-aggressive
behavior. Eur J Pediatr. 2003;162:559-561.
5. Adams JB, Romdalvic J, Sadagopa VH, Legator MS. Mercury,
lead, and zinc in baby teeth of children with autism versus controls.
J Toxicol Environ Health. 2007;70:1046-1051.
6. Holmes AS, Blaxill MF, Haley BE. Reduced levels of mercury
in first baby haircuts of autistic children. Int J Toxicol. 2003;
7. Walker SJ, Segal J, Aschner M. Cultured lymphocytes from autistic
children and nonautistic siblings up-regulate heat shock protein
RNA in response to thimerosal challenge. Neurotoxicol. 2006;
8. Fido A, Al-Saad S. Toxic race elements in the hair of children
with autism. Autism. 2005;9:290-298.
9. Kern JK, Grannerman BD, Trivedi MH, Adams J. Sulfhydrylreactive
metals in autism. J Toxicol Environ Health. 2007;70:
10. Gravetter FJ, Wallnau LB. Essentials of Statistics for the Behavioral
Sciences. 4th ed. Pacific Grove, CA: Wadsworth; 2005.
11. Tabachnik B, Fidell LS. Using Multivariate Statistics. New York:
Prentice Hall; 2006.
12. Steering Committee of the Physicians’ Health Study Research
Group. Preliminary report: findings from the aspirin component
of the ongoing physicians’ health study. N Engl J Med.
13. Rosnow RL, Rosenthal R. Statistical procedures and the justification
of knowledge in psychological science. Am Psychol.
© 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
Downloaded from by Lourdes Salvador on November 18, 2007

From the Department of Psychology, University of Northern Iowa,
Cedar Falls, Iowa.
Address correspondence to: M. Catherine DeSoto, Department of
Psychology, University of Northern Iowa, Cedar Falls, IA 50614; e-mail:
DeSoto, CM, Hitlan RT. Blood levels of mercury are related to diagnosis
of autism: a reanalysis of an important data set. J Child Neurol.