Toxicologic Pathology, 32:448–466, 2004
C by the Society of Toxicologic Pathology
Copyright ISSN: 0192-6233 print / 1533-1601 online
DOI: 10.1080/01926230490465874
Relationships Between Organ Weight and Body/Brain Weight
in the Rat: What Is the Best Analytical Endpoint?
STEVEN A. BAILEY,1 ROBERT H. ZIDELL,2 AND RICHARD W. PERRY1
2
1
Wyeth Research, Chazy, New York 12921
Bristol-Myers Squibb, East Syracuse, New York 13057
ABSTRACT
Analysis of organ weight in toxicology studies is an important endpoint for identification of potentially harmful effects of chemicals. Differences
in organ weight between treatment groups are often accompanied by differences in body weight between these groups, making interpretation of organ
weight differences more difficult. Using data from control rats that were part of 26 toxicity studies conducted under similar conditions, we have
evaluated the relationship between organ weight and body/brain weight to determine which endpoint (organ weight, organ-to-body weight ratio, or
organ-to-brain weight ratio) is likely to accurately detect target organ toxicity. This evaluation has shown that analysis of organ-to-body weight ratios
is predictive for evaluating liver and thyroid gland weights, and organ-to-brain weight ratios is predictive for evaluating ovary and adrenal gland
weights. Brain, heart, kidney, pituitary gland, and testes weights are not modeled well by any of the choices, and alternative analysis methods such as
analysis of covariance should be utilized.
Keywords. Sprague–Dawley rats; organ weight; body weight; brain weight; statistics; historical control data; toxicology studies.
INTRODUCTION
An important requirement in toxicological experiments
is the ability to assess the effects of xenobiotics on specific organs. For many organs, this is done through macroscopic examination of the organs, measuring organ weight,
and histopathologic examination of the tissue. Organ weight
can be the most sensitive indicator of an effect of an experimental compound, as significant differences in organ weight
between treated and untreated (control) animals may occur
in the absence of any morphological changes.
The comparison of the organ weights of treated animals
with untreated animals is often complicated by differences
in body weights between groups. Therefore, other parameters that are commonly used for analysis of organ weight are
the ratio of the organ weight to body weight (to account for
differences in body weight) and the ratio of the organ weight
to the brain weight (which represents a surrogate measure
for lean body mass, which is not usually affected by xenobiotics). These ratios are generally described as relative organ
weights. However, organ weight ratios may lead to faulty conclusions. The possibility that the use of relative organ weight
( i.e., organ weight expressed as a percentage of body weight
or brain weight) for rats and other species may lead to misinterpretation has been presented frequently in the literature
(Angervall and Carlström, 1963; Setnikar and Magistretti,
1965; Krames and Van Liere, 1966; Dikstein et al., 1967;
Feron et al., 1973; Stevens, 1976, 1977; Trieb et al., 1976;
Schärer, 1977; Shirley, 1977, 1982; Takizawa, 1978; Hutson
et al., 1981; Uemitsu and Nakayoshi, 1984; Brown et al.,
1985; Salsburg, 1986; Famula et al., 1988).
For the use of the organ-to-body weight ratio to be valid,
a proportional relationship between organ weight and body
weight is assumed. Thus a 20% difference in body weight
between animals should be accompanied by a 20% difference
in the weight of each organ. This relationship assumes that
the ratio of organ weight (Y) to body weight (X) within each
treatment group is some constant µ, i.e.,
Y/X = µ
or likewise
Y = µX
In fact, an analysis of variance on the ratio values is mathematically equivalent to a weighted analysis of covariance,
using the body weight as the covariate, assuming a zero intercept, and using the inverse of the squared body weight as the
weight factor in the model. Similar assumptions with regard
to proportionality are required for the organ-to-brain weight
ratio.
We have investigated how well various organs satisfy the
assumption of proportionality in control animals and therefore assessed the value of the model underlying the ratio analysis (using both body weight and brain weight as the relative
index). Based on these results, we make recommendations
regarding the analytical endpoint that is most predictive for
each organ.
MATERIALS AND METHODS
The data used in our evaluation consisted of data from control rats in 1-month studies, which were conducted between
January 1992 and July 1999 at Wyeth Research in Chazy,
NY. There were 26 such studies, which contained data from
307 male and 266 female animals.
Address correspondence to: Steven A. Bailey, Wyeth Research, 641
Ridge Road, Chazy, New York 12921, USA; e-mail: [email protected]
448
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OPTIMAL ORGAN WEIGHT ANALYSES
The rats (Crl:CDR [SD]BR) in these studies were all
obtained from Charles River Laboratories, St-Constant,
Quebec, Canada. The rats were 7–8 weeks of age at the time
of study initiation. Therefore, the rats were of similar age
at the time of sacrifice and organ weight measurement. The
rats were maintained in well-regulated environmental conditions; temperature and relative humidity in the study rooms
were set at 72◦ F and 50%, respectively, and were continuously monitored. Values outside the acceptable limits were
documented; all deviations were of short duration and low
magnitude and did not adversely affect the health of the animals or the interpretation of the study data. The rats were
generally housed 2 per cage in suspended stainless-steel wiremesh cages (16 × 91/2 × 7 ). Certified Rodent Diet #5002
(PMIR Feeds, Inc.) and drinking water were available ad libitum to all animals.
Rats in these studies were from control groups used
in toxicity experiments. The use of controls as our data
source minimized or eliminated the influence of concurrent conditions (e.g., disease, obesity, stress, age-related
changes of organ function, hormonal status, or nutritional
status) that can alter absolute organ weights or body weight.
The rats generally received daily administration of formulated vehicles used in standard toxicity studies. These vehicles were given orally (usually by gavage), but in several studies the rats were in untreated control groups. The
vehicle formulations were those routinely used in toxicologic experiments (e.g., carboxymethylcellulose, natrosol,
polysorbate 80). These vehicles are widely considered to be
innocuous components for which a comparative untreatedcontrol group is unnecessary. In no case were toxic effects observed that were attributable to treatment with the
vehicles.
During the studies, blood samples were routinely collected during weeks 1 and 4 to monitor clinical pathology
parameters. Routine blood collection volumes were <10%
of the total blood volume and were not associated with adverse health effects on the animals. The final blood collection was routinely performed several days prior to sac-
449
rifice, so did not adversely influence the interpretation of the
data.
At termination of the studies, the rats were weighed (on
the morning of necropsy), euthanized by CO2 asphyxiation,
exsanguinated to minimize the contribution of blood to the
organ weight (Sullivan, 1985), and subjected to complete
necropsy. Because these rats were from control groups in
various studies, necropsies occurred over the course of the
normal working day to accommodate randomized necropsy
order. The rats were not fasted prior to necropsy, but the possibility that some organ weight changes can occur over the
course of the day is acknowledged, and has been reported previously for liver (Rothacker et al., 1988). Following necropsy,
protocol-specified organs (see Table 1) were examined, dissected free of fat and weighed using calibrated balances. Tissues were fixed in 10% neutral buffered formalin, sectioned,
embedded with paraffin, and stained with hematoxylin and
eosin using standardized pathologic techniques. The organs
were examined microscopically by veterinary pathologists,
and were subjected to peer review by an independent veterinary pathologist. Organs with any macroscopic abnormality
(such as those with discoloration, those that were damaged
during necropsy, or those with evidence of confounding disease) were excluded from the evaluations described in this
report to minimize the influence of these changes on organ
weights.
To evaluate the general linear relationship between organ
weight and body weight for each organ and sex combination,
the best fitting line was fit to the data. This was done using a
simple linear regression model of the form
Y = α + βX + (1)
where: Y is the organ weight; X is the terminal body weight;
α, β are the intercept and slope parameters, respectively; is
the error term.
Tests were performed to determine if the intercept (α) and
slope (β) terms are significantly different from 0. If the slope
term is not significantly different from 0, this implies that no
TABLE 1.—Results of simple linear regression analysis to evaluate the relationship of organ weight to body weight.a
Organ
Sex
Sample size
Intercept estimateb
p-Value for intercept = 0
Slope estimatec
p-Value for slope = 0
Adrenal glands
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
307
266
307
266
289
252
314
273
252
221
280
251
304
266
307
265
18.889 mg
45.352 mg
603.35 mg
410.82 mg
1,206.29 mg
869.00 mg
−824.4 mg
897.3 mg
−0.751 mg
−6.612 mg
−1.566 mg
−5.371 mg
2,327.22 mg
49.771 mg
1,800.465 mg
1,652.495 mg
0.004
<0.001
<0.001
<0.001
<0.001
<0.001
0.450
0.079
0.804
0.044
0.762
0.326
<0.001
0.001
<0.001
<0.001
0.095
0.090
1.75
1.94
4.69
4.33
40.5
32.4
0.031
0.085
0.063
0.102
2.17
0.194
0.568
0.927
<0.001
0.007
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Heart
Kidneys
Liver
Pituitary glands
Thyroid gland
Testes
Ovaries
Brain
a
Results are not presented for the prostate gland, spleen, seminal vesicles, and uterus due to insufficient sample sizes.
A linear regression of organ weight (Y-axis) against body weight (X-axis) was performed. The least-squares fit of these data gave the Y-intercept values shown in this column, and
statistical analyses of these data are given in the subsequent column, in which statistically significant values are noted in bold.
c
A linear regression of organ weight (Y-axis) against body weight (X-axis) was performed. The least-squares fit of these data gave the slopes shown in this column, and statistical analyses
of these data are given in the subsequent column, in which statistically significant values are noted in bold.
b
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BAILEY ET AL.
TABLE 2.—Results of simple linear regression analysis to evaluate the relationship of organ weight to brain weight.a
Organ
Sex
Sample size
Intercept estimateb
p-Value for intercept = 0
Slope estimatec
p-Value for slope = 0
Adrenal glands
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
307
266
307
266
289
252
314
273
252
221
280
251
304
266
2.207 mg
18.983 mg
935.327 mg
311.399 mg
1,669.707 mg
688.120 mg
8,504.44 mg
4,743.30 mg
4.828 mg
−2.985 mg
−15.647 mg
5.237 mg
2,184.283 mg
25.920 mg
0.870
0.220
<0.001
0.002
<0.001
0.003
0.004
0.001
0.418
0.662
0.139
0.634
<0.001
0.279
0.028
0.026
0.195
0.302
0.732
0.652
3.70
2.10
0.003
0.009
0.020
0.007
0.513
0.038
<0.001
0.002
0.012
<0.001
<0.001
<0.001
0.012
0.004
0.244
0.016
<0.001
0.208
0.001
0.003
Heart
Kidneys
Liver
Pituitary gland
Thyroid gland
Testes
Ovaries
a
Results are not presented for the prostate gland, spleen, seminal vesicles, and uterus due to insufficient sample sizes.
A linear regression of organ weight (Y-axis) against brain weight (X-axis) was performed. The least-squares fit of these data gave the Y-intercept values shown in this column, and
statistical analyses of these data are given in the subsequent column, in which statistically significant values are noted in bold.
c
A linear regression of organ weight (Y-axis) against brain weight (X-axis) was performed. The least -squares fit of these data gave the slopes shown in this column, and statistical
analyses of these data are given in the subsequent column, in which statistically significant values are noted in bold.
b
relationship is detected between the organ weight and body
weight. If the slope term is significantly different from 0,
but the intercept term is not, this implies that a proportional
relationship between the organ weight and body weight is
detected. If both the slope and intercept terms are significantly different from 0, this implies a nonproportional relationship between organ weight and body weight is detected. To evaluate the general linear relationship between
organ weight and brain weight, a similar model was fit using
the brain weight instead of body weight as the independent
variable.
RESULTS
The results from the simple linear regression model to
evaluate the linear relationship of organ weight versus body
weight (equation 1) are presented in Table 1 and similar results for organ weight versus brain weight are presented in
Table 2. Scatter plots of organ weight against body weight
and against brain weight for each organ and sex combination
are presented in Figures 1–9. Three lines are presented in
each of the plots:
r A solid line represents the best fit line to the data based on
than zero when modeled against body weight (Table 1), and
against brain weight (Table 2). Therefore, relationships exist
between these organ weights and both body and brain weight;
these relationships are not proportional. Figures 3, 4, and 8
demonstrate the relationships between these organs and both
brain and body weight.
For adrenal gland (both sexes), and ovary, the slope and
intercept terms were significant when modeled against body
weight (Table 1). When modeled against brain weight, the
slope term was significant and the intercept term was not
significant (Table 2). Therefore, relationships exist between
these organ weights and both body and brain weight. The relationship to brain weight is proportional and the relationship
to body weight is not proportional. Figures 1 and 9 demonstrate the relationships between these organs and both brain
and body weight.
For liver weight (both sexes), the slope term was significant and the intercept term was not significant when modeled against body weight (Table 1). When modeled against
brain weight, the slope and intercept terms were significant
(Table 2). Therefore, relationships exist between the liver
weight and both body and brain weight. The relationship to body weight is proportional and the relationship
a simple linear regression (as described in equation 1).
r A dotted line represents the best fit under the constraints of
the ratio model, which forces the line to pass through the X–
Y origin. This line was generated using a weighted regression with the intercept parameter set equal to zero, which
is mathematically equivalent to the relationship modeled
by the ratio analysis.
r A dashed line represents the best fit under the unadjusted analysis, which is represented by the mean organ
weight.
For brain (both sexes), the slope and intercept terms were
significantly different than zero when modeled against body
weight (Table 1). Therefore, a relationship exists between
brain and body weight and this relationship is not proportional. Figure 2 demonstrates the relationship between brain
and body weight.
For heart (both sexes), kidney (both sexes), and testes,
the slope and intercept terms were significantly different
TABLE 3.—Optimal use of absolute organ weight, organ-to-body weight ratio,
and organ-to-brain weight ratio analyses.
Organ
Adrenal gland
Heart
Kidneys
Liver
Pituitary Gland
Thyroid Gland
Testes
Ovaries
Brain
Relative to
body weight
Relative to
brain weight
√
Alternative
statistical methods
√
√
√
√
Statistical analyses indicates that the model corresponding to this endpoint fits the data.
This endpoint is recommended for evaluation of organ weight differences in the presence
of body weight differences among groups.
Statistical analyses indicate that the models exhibit lack of fit for analysis of absolute
weight, organ-to-body weight ratio, or organ-to-brain weight ratio. Analysis should utilize
alternative statistical methods.
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OPTIMAL ORGAN WEIGHT ANALYSES
to brain weight is not proportional. Figure 5 demonstrates
the relationship between liver and both body and brain
weight.
For pituitary gland weight, results differed between the
sexes. Male pituitary gland weight had a significant slope
term and a non-significant intercept term when modeled
against body weight (Table 1). Therefore, there is a proportional relationship between male pituitary gland weight and
body weight. Female pituitary gland weight had significant
451
intercept and slope terms when modeled against body weight
(Table 1), and a significant slope term and nonsignificant intercept term when modeled against brain weight (Table 2).
Therefore, relationships exist between the female pituitary
gland weight and both body- and brain-weight. The relationship to body weight is not proportional; the relationship to
brain weight is proportional. Figure 6 demonstrates the relationship between pituitary gland and both body and brain
weight.
FIGURE 1.—Adrenal weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section. (Continued)
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BAILEY ET AL.
TOXICOLOGIC PATHOLOGY
FIGURE 1.—Continued
For thyroid gland weight, results also differed between the
sexes. Male thyroid gland weight had significant slope and
nonsignificant intercept terms when modeled against both
body (Table 1) and brain (Table 2) weight. Therefore, there
is a proportional relationship between male thyroid gland
weight and both body and brain weights. Female thyroid
gland weight had a significant slope and nonsignificant intercept term when modeled against body weight (Table 1).
Therefore, there is a proportional relationship between female thyroid gland weight and body weight. Figure 7 demonstrates the relationship between pituitary gland and both body
and brain weight.
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OPTIMAL ORGAN WEIGHT ANALYSES
453
FIGURE 2.—Brain weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section.
DISCUSSION
A major goal of a toxicity study is to identify target organs, which typically requires the use of toxic doses of
the test compound. If body weight is affected by this treatment, interpretation of organ weight data can become com-
plicated. Differences in body weight are often present between treatment groups and the concurrent control group,
and often between groups receiving different doses of the experimental compound. These types of effects can be induced
in a number of ways. For example, body weight changes
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BAILEY ET AL.
can occur through alterations in growth (e.g., agents that
modify secretion of growth hormone or somatostatin), alterations of hormonal status (e.g., agents that modify secretion of sex steroids and thereby alter maturational patterns),
changes in neurotransmitters that affect food consumption
(e.g., agents that affect central serotoninergic or dopaminergic systems), reduced palatability of diets containing the
experimental compound, or through nonspecific systemic
toxicity.
TOXICOLOGIC PATHOLOGY
For each organ, an evaluation of the results listed in
Tables 1 and 2 was performed to determine which endpoint
is optimal for analysis. As outlined above, if the slope term
is not significantly different from 0, then there is no evidence of a relationship between the organ weight and body
(or brain) weight and the absolute analysis would be optimal. If the slope term is significantly different from 0, but
the intercept term is not, there is a relationship between the
organ weight and body (or brain) weight and the relationship
FIGURE 3.—Heart weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section. (Continued)
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455
FIGURE 3.—Continued
is proportional. In this case the body (or brain) weight ratio
analysis is optimal. If both the slope and intercept terms are
significantly different from 0, this implies that there is a relationship between organ weight and body (or brain) weight
and that the relationship is not proportional. In this case, neither the absolute nor the body (or brain) weight ratio analyses
are optimal.
Based on the results reported in Tables 1 and 2, absolute
organ weight is never an optimal endpoint for the evaluation of organ weight changes in the presence of body weight
differences between the groups. Liver and thyroid gland are
optimally analyzed using organ-to-body weight ratio to evaluate the effects of a test chemical on weights of the organs.
Similar conclusions have been previously reported for liver
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BAILEY ET AL.
(Weil and Gad, 1980). Note that the results listed in Table 2
imply that male thyroid gland weight could also be optimally
analyzed using the organ-to-brain weight ratio, however for
simplicity we suggest the organ-to-body weight ratio analysis for thyroid gland weight for both sexes. Adrenal glands
TOXICOLOGIC PATHOLOGY
and ovaries are optimally evaluated using the organ-to-brain
weight ratio.
For brain, heart, kidneys, and testes, the results reported in
Tables 1 and 2 indicate that the models exhibit lack of fit for
all of the endpoints. Alternative statistical analysis methods,
FIGURE 4.—Kidney weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section. (Continued)
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457
FIGURE 4.—Continued
as outlined below, should be considered for the analysis of
these organs.
The results listed in Tables 1 and 2 for pituitary gland
weight are problematical because of the difference in results
between the sexes. Results in Table 1 imply that male pituitary
gland weight would be optimally analyzed with the body
weight ratio, but female pituitary gland weight would not.
Results in Table 2 imply the opposite, that female pituitary
gland weight would be optimally analyzed with the brain
weight ratio, but that male pituitary gland weight would not.
We see no reason to believe that male and female pituitary
gland weights should be analyzed differently. Since there is
no clear choice for an optimal analysis endpoint, alternative
statistical analysis methods should be considered.
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BAILEY ET AL.
Many of the organs in the set we investigated had statistically significant intercept terms when fitting regression lines
for organ weight on body weight or organ weight on brain
weight. In these cases, the assumption of a proportional relationship may be in error and analysis of relative organ weight
may lead to erroneous conclusions. To illustrate, let us consider adrenal gland weight to body weight ratios. In our anal-
TOXICOLOGIC PATHOLOGY
ysis of the relationship between adrenal gland weight and
body weight, we had a significant intercept term ( p < 0.01
for intercept = 0 in both sexes) and a significant slope term
( p < 0.01 for slope = 0 in both sexes). Therefore, in equal
aged animals, differences in body weight between animals
are not associated with proportional differences in adrenal
gland weight. Indeed, the expected change in adrenal weights
FIGURE 5.—Liver weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section. (Continued)
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459
FIGURE 5.—Continued
associated with a 10% change in body weights would only
be 3.3% in males and 1.6% in females based on the historical
data. The nonproportional relationship between body weights
and adrenal gland weights has been described in earlier publications (Christian, 1953; Cullen et al., 1971) highlighting the
potential error of using relative weights to interpret effects
on the adrenal glands.
This potential for error in evaluating the effects of chemicals on the weights of adrenal glands and other organs
has been reported in many previous reports (Angervall and
Carlström, 1963; Setnikar and Magistretti, 1965; Krames
and Van Liere, 1966; Dikstein et al., 1967; Feron et al.,
1973; Stevens, 1976, 1977; Trieb et al., 1976; Schärer, 1977;
Shirley, 1977, 1982; Takizawa, 1978; Hutson et al., 1981;
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BAILEY ET AL.
Uemitsu et al., 1984; Brown et al., 1985; Salsburg, 1986;
Famula et al., 1988). In the cases where the organ-to-body
weight relationship does not justify the analysis of ratio values, several alternative methods have been suggested. The
most common alternative is the analysis of covariance, using
the body weight as the covariate, which has been suggested
to be a better method to analyze organ weights (Angervall
TOXICOLOGIC PATHOLOGY
and Carlström, 1963; Shirley, 1977, 1982; Takizawa, 1978;
Shirley and Newnham, 1984; Brown et al., 1985; Salsburg,
1986; Famula et al., 1988). This analysis requires the assumption of a linear relationship between the organ weights and
the body weights, but does not require the more restrictive
assumption of a proportional relationship. Other alternative
methods for statistical analysis have been suggested to avoid
FIGURE 6.—Pituitary weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section. (Continued)
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461
FIGURE 6.—Continued
complications associated with use of absolute weights or relative weights (Dikstein et al., 1967; Spencer, 1968; Simpson
and Spears, 1973; Stevens, 1976, 1977; Trieb et al., 1976;
Hutson et al., 1981), however none of these have been widely
used to date.
The results of this evaluation are summarized in Table 3.
We have concluded that liver and thyroid gland weights are
optimally analyzed using organ-to-body weight ratios, and
ovary and adrenal gland weights are optimally analyzed using
organ-to-brain weight ratios. Brain, heart, kidney, pituitary
gland, and testes weights are not modeled well by any of the
choices, and alternative analysis methods, such as analysis
of covariance, should be utilized. Note that the less rigorous
assumption required by the analysis of covariance make this
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BAILEY ET AL.
analysis valid and appropriate for all organs considered in
this paper.
In addition to the direct toxic effects of chemicals, which
can induce changes in body weight and associated changes in
organ weight, other factors influence body weights and organ
weights by secondary means.
In rats and other species, the absolute weights of many,
but not all, organs are affected by growth during the normal life span during which toxicity studies are conducted
TOXICOLOGIC PATHOLOGY
(Simpson and Spears, 1973; Schärer, 1977; Iwata et al., 1993;
Kihara et al., 1993; Teramoto et al., 1996). Comparison of
organ weights in these studies shows small differences in the
absolute weights of some organs (e.g., adrenal glands), but
large and often proportional differences in absolute weights
of other organs (e.g., liver).
Conceptually, a compound that causes reduced body
weight gain would not change the absolute weight of those organs that do not significantly change in weight during normal
FIGURE 7.—Thyroid weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section. (Continued)
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OPTIMAL ORGAN WEIGHT ANALYSES
463
FIGURE 7.—Continued
growth (e.g., brain, testes, ovaries, or adrenals), unless there
was another specific toxic effect on the organ. Since the numerator (organ weight) is unchanged, but the denominator
(body weight) is altered, a change in relative organ weight
will result. Using relative organ weights to interpret the effect of the compound can introduce error and mislead the
investigator regarding the effect of the compound on the or-
gan. Similarly, relative weights of most organs change as
normal animals grow (Dikstein et al., 1967; Simpson and
Spears, 1973; Iwata et al., 1993; Teramoto et al., 1996).
These data imply that growth retardation induced by treatment with experimental compounds will result in artifactual changes in the relative weights of some organs, unless there are specific toxic effects on those organs. These
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BAILEY ET AL.
TOXICOLOGIC PATHOLOGY
FIGURE 8.—Testes weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section.
types of effects have been noted on many occasions in 1
month toxicity studies conducted at our facility (unpublished
data).
Growth retardation has also been examined by either incorporating indigestible or nutrient-poor components into the
diet of growing rats, (Feron et al., 1973; Schweisthal et al.,
1982) or by feed restriction (Dikstein et al., 1972; Schärer,
1977; Oishi et al., 1979; Chatamra et al., 1984; Keenan
et al., 1995). In many of these reports, the absolute weights
of many organs were decreased compared to controls, but
the magnitude of the effect varied considerably between organs. Using liver and adrenal glands as 2 examples of the
divergent effects of growth retardation, large effects on liver
weights are often reported with little or no effects on adrenal
gland weights. It must be acknowledged, however, that the
rats in these studies may be exposed to other factors that
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Vol. 32, No. 4, 2004
OPTIMAL ORGAN WEIGHT ANALYSES
465
FIGURE 9.—Ovary weights. —— Best fit line; - - - - Ratio fit; — - - — - - Absolute fit. For further details, see the results section.
can complicate the analysis. For example, dietary restriction may induce significant stress, which can affect adrenal
gland weights (Boorman et al., 1990). Liver weights may
be influenced by dietary factors that induce hepatic enzymes
(Amacher et al., 1998). Additionally, alterations of growth
rate are likely to alter maturational patterns, which can influence the weights of the adrenal glands and the liver (Hart,
1990).
Remarks
The results in this paper are related specifically to rats,
and on that basis the data can be considered the most reliable for this species. Unpublished results from our laboratory
confirm that the body weight and organ weight correlations that we describe for rats are generally applicable to other species (such as mice, dogs, and monkeys).
Similar relationships for all the organs will probably be
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466
BAILEY ET AL.
found in other species, and these relationships are being
examined.
CONCLUSIONS
Evaluation of organ weight changes in the presence of
body weight differences has resulted in the use of additional
tools such as organ-to-body weight and organ-to-brain weight
ratios to assess treatment effects in toxicology studies. Understanding the relationship between absolute organ weight,
body weight, and brain weight as well as which parameter
bests predicts a true effect on organ weight will lead to improved organ weight interpretation. Based on analysis of control animal data, liver and thyroid gland weights are best compared using organ-to-body weight ratios, and adrenal gland
and ovary weights are best compared using organ-to-brain
weight ratios. Analysis of brain, heart, kidney, pituitary gland
and testes weights should utilize alterative methods such as
analysis of covariance.
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