Do larger people have more naevi? Naevus frequency versus naevus density

Sd Waltera, R Ashboltb, T Dwyerb and Ld Marrettc

a Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada.
b Menzies Centre for Population Health Research, University of Tasmania, Hobart, Tasmania, Australia.
c Cancer Care Ontario, Toronto, Canada.

Correspondence: Dr SD Walter, Department of Clinical Epidemiology and Biostatistics, McMaster University, HSC-2C16, Hamilton, Ontario, Canada L8N 3Z5. E-mail: walter{at}mcmaster.ca


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Background It is unclear which of the number or the density of naevi on the skin is the more appropriate measure of risk of melanoma. Furthermore, the relationship between the number of naevi and their density in an individual has not been explored. Thus, for example, it is unknown if larger people tend to have more naevi by virtue of having a larger skin area, or if the density of naevi is similar in people of different body sizes. In this study, we explored the relationship between the number and the density of naevi in a sample of adolescents.

Subjects and Methods A sample survey of naevi in 472 grade 9 secondary school students (aged 14–15 years) was conducted in Tasmania, Australia during 1992, and a subset of these individuals was followed up in 1997. Counts of naevi of various sizes were taken on the arm, leg, and back. Naevus density was estimated by using an algorithm to estimate body surface area from the height and weight of an individual. More general relationships of the naevus counts to height and weight were also explored. Finally, we considered whether the relationship between naevus density and the anthropometric variables could be confounded by exposure to ultraviolet radiation.

Results The mean number of naevi was very similar in the two samples. Naevus density was slightly lower in the 1997 sample, mainly because of increasing body size in the cohort. The numbers of naevi were only weakly related to height and weight in males, and there was essentially no relationship in females. Regression analysis showed significant relationships of weight to the back naevus counts in males in 1992 and 1997, and to the arm naevus count in males in 1997; otherwise, none of the regression coefficients for height and weight were statistically significant. This picture did not change following adjustment for potentially confounding variables indicating time spent outdoors or in the sun. Furthermore, there was no evidence that time spent in the sun was related to the body mass index.

Conclusions It appears that the number and density of naevi in an individual are unrelated. Accordingly, with the present state of knowledge concerning the risk of melanoma, both the number and density of naevi should be considered as equally valid in future studies as markers of the risk of melanoma, and in studies on the natural history of naevi. If the disease mechanism is systemic, and not related to particular naevi, naevus density might form the better marker of risk. However, if the disease mechanism is related to effects on particular naevi, then the risk would vary in proportion to the number of naevi.

Keywords Melanoma, naevi, anthropometry

Accepted 7 June 2000


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The relationship between naevi and the risk of melanoma has been studied epidemiologically, primarily using the case-control design. There have also been a few studies of the natural history of naevi, using either a cross-sectional survey approach or a cohort design. These studies have, however, used various summary measures of naevi in an individual. Most investigators have used a frequency count of the number of naevi, either on part of the body such as the arms, or over the entire body surface.1–8 Others have estimated the relevant body surface area (BSA) in order to compute the naevus density per square metre.9–15

It is unclear which of naevus frequency or density might constitute the most appropriate marker of risk for melanoma. If the mechanism of disease induction relates to cellular changes in particular naevi (e.g. as a result of stimulation by exposure to ultraviolet radiation), then the disease risk would vary essentially as the number of naevi. However, the disease mechanism might be more systemic, and not related to specific naevi; in this case, the density of naevi might form a better marker of risk for the individual.

The natural history studies have mainly focused on changes in the number or density of naevi in an individual and they suggest that in Caucasian populations naevus frequency increases rapidly in childhood9–11,16 and peaks in the teens6,7,14 or early twenties15 and then progressively declines during later life. There are some suggestions that there is a latitude gradient for the age at which the number of naevi peak.12

Little is known about the relationship between naevus frequency and density. It is particularly important to understand this relationship in children, whose body size changes rapidly as they grow. So, for instance, it is not known if the increasing numbers of naevi seen previously in the teen years is really indicative of increasing density or an increase in number consequent on an increasing BSA.

In this paper, we examine the relation between number and density of naevi using data from a recent survey of adolescents in Hobart, Tasmania.13 In particular, we examine the distribution of naevi by body site at two different ages. We evaluate if the number of naevi in an individual is proportional to estimated BSA, and also consider more general relationships of naevus counts to height and weight.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
A survey of naevi in grade 9 secondary school students (aged 14–15 years) was conducted during 1992. Subjects were selected using a two-stage sampling method. Ten schools were chosen using systematic sampling with a probability proportional to the enrolment of students aged 15 years. The second stage of sampling consisted of the random selection of approximately 60 students from each school. Five years later, in 1997, this cohort was approached to participate in a follow-up study to determine changes in naevus prevalence and density and to use a new method of estimating the melanin content of the skin.17 A letter of invitation and information sheet were sent to the original 1992 address of the parents. If the subject was not located, the electoral roll and telephone ‘white pages’ were searched for the subject or the subject's parents. As the subjects were originally ascertained at school, the school and fellow former class-mates were also approached for any information on the participants' whereabouts.

Naevi were identified using the IARC protocol18 and were counted in two size categories: diameter >=2 mm but <5 mm, and diameter >=5 mm. Separate counts of palpable lesions were conducted. Protocols for naevus counts have been described elsewhere.13 Naevi were counted by a research nurse on the left arm, right leg and back (including shoulders) in 1992 and on the left arm, back (including shoulders) in 1997. In our analysis, we consider total naevus counts classified by size as 2+ mm, and 5+ mm.

The total BSA (in m2) of an individual can be estimated as

(1)
with height (H) measured in cm and weight (W) in kg,19–22 where a is a constant. This algorithm was originally developed for the purpose of estimating the area of skin affected by burns.23 Gehan and George21 estimated a as 0.016821; they also considered more general power relationships of H and W to BSA.

Skin areas for specific body sites are estimated as certain fixed proportions of the total BSA.23 We used 13% as the percentage for the back, 31% for two legs (excluding the feet) and 14% for two arms (excluding the hands). Once the relevant surface areas have been computed, one obtains the naevus density as (naevus count)/(BSA).

Univariate summaries of the various naevus counts were computed. The log transformation of the counts was also investigated, to see if the transformation would eliminate skewness in the distributions, as has been noted previously.12,14 Regressions were computed using the log of the naevus count (N) as the dependent variable, and the logarithms of height (H) and weight (W) as independent variables. Some analyses incorporating adjustments for skin colour and age were done. Skin colour was assessed using the Commission International d'Eclairage L* parameter.13

The basic rationale for the regression model is as follows. If naevus density was constant between individuals, then the naevus count N should be proportional to the individuals' body surface area BSA, i.e.

where k is a constant. From equation (1) we see that this is equivalent to N = k {surd}(H x W). Taking logs of both sides gives

(2)
Regressions of log(N) should therefore show coefficients of 0.5 for log(H) and log(W), if an individual's number of naevi is indeed proportional to BSA, and if the estimation of BSA is correct. Coefficients greater than 0.5 would indicate a stronger dependence on H and/or W than expected by proportionality, and coefficients lower than 0.5 would indicate a correspondingly weaker dependence. In general, using a regression of log(N) on log(H) and log(W) should suggest the optimal powers of H and W from which to estimate the dependence of N on height and weight.

Regressions of log(N) on log(H) and log(W) were computed for the 1992 and 1997 data, for each body site. We also computed regressions of N on BSA directly. Further regressions and correlations were computed for the sum of arm, back and leg counts of large (5+ mm) in the 1992 data. To avoid involv-ing the logarithm of zero naevus counts, the transformation log(N + c) was used, with various positive values of c. Sensitivity of the results to the selected value of c was investigated.


    Results
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
In all, 472 grade 9 school children participated in the 1992 naevus study, a response rate of 71.8%. In a follow-up study 5 years later, all but 62 (13%) of the original participants were within Tasmania; of these 79.3% (n = 325) participated in the follow-up.

For data analysis, all subjects with one or more grandparents born in Asia or Southern Europe were excluded from the data set as the study focus was on determinants of naevi in the higher risk Caucasian population. This left 426 subjects (208 males, 218 females) available for analysis in 1992, and 291 subjects (138 males, 153 females) in 1997. In the 1992 sample, 97% were aged 14–15 years.

We compared the distribution of the 1992 baseline variables between the subjects who were followed up in 1997 and those who were not followed up. For most of the variables examined, there was no significant difference between the two groups. This was true, for example, for the time spent in the sun, the lifetime history of sunburns, and the number of naevi counted in 1992. There were, however, some slight differences in height and weight; in both sexes, the individuals who were followed up were slightly taller than the individuals not followed up, and in females the subjects followed up were slightly heavier. Because height and weight were key variables in the analysis, we subsequently carried out separate analyses of the 1992 and 1997 data sets.

Table 1Go summarizes the participants' heights and weights, and derived BSA in 1992 and 1997. Limiting attention to subjects observed both in 1992 and 1997, males experienced mean increases of 8.0 cm (5% of 1992 value) in height, 13.4 kg (22%) in weight, and 0.23 m2 (14%) in BSA. The corresponding gains for females were all lower: 2.0 cm (1%) in height, 7.0 kg (12%) in weight and 0.10 m2 (6%) in BSA.


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Table 1 Mean (SD) of height (cm), weight (kg) and estimated body surface area (BSA)(m2) for study participants, by sex and year of study
 
Table 2Go summarizes the counts of naevi of size >=2mm by body site. For the two sites where measurements were taken in both waves of the survey (arm and back), only very small differences in the means were observed, relative to the variation between participants. Arm and leg naevus counts were similar for males and females, but back counts were somewhat lower in females.


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Table 2 Mean (SD) of number of naevi 2+ mm in size, by body site, sex and year of study
 
The distributions of naevus counts were positively skewed. Transformations of the data using log(N + c) (with c = 1, 2, 5 and 10) considerably reduced the skewness. Neither the amount of skewness nor the relationship of the transformed counts to BSA, weight and height were greatly dependent on the value of c. Accordingly the log transformation with c = 1 was used for the regression analyses.

Table 3Go summarizes the naevus density by body site in the two surveys. As was seen for naevus frequencies, naevus density was lower on the back in females compared to males, but arm and leg densities were similar for males and females. There were small percentage differences in naevus density between the two surveys. As seen from the earlier Tables, this is essentially due to increase in body size, with the number of naevi remaining essentially unchanged. The percentage differences in naevus density are slightly larger for females, even though their height, weight and BSA changed less than males.


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Table 3 Mean density (m–2) (SD) of naevi 2+ mm in size, by body site, sex and year of study
 
Table 4Go shows the correlations of the naevus counts with height, weight and BSA, for each year of the study. Correlation values were generally very low in females. In males the correlations were slightly higher, particularly for back naevi. Note that these correlations are on the original naevus counts, without transformation, and so may be influenced by the skewness in the data, and by individuals with particularly large numbers of naevi.


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Table 4 Correlations of height, weight and body surface area (BSA) with naevus counts, by body site, sex and year of study
 
Table 5Go shows the results of regressions of log(N) on log(H) and log(W). As might be expected from the earlier results, the proportion of variation explained by the independent variables (as assessed by the R2 statistic) was low, especially for females. In the 1992 data, the relationship to the back naevus count in males was slightly stronger; many of the coefficients were close to the value of 0.5 anticipated under model (2), but the variation in the estimates was substantial. Only the coefficient for log(W) on the back count in males was statistically significantly different from zero. Results were somewhat similar in the 1997 data, although here there was a stronger and statistically significant effect of log(W) in males for both the arm and back data. Both relationships were again extremely weak in females.


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Table 5 Regression coefficients (SE) of log naevus counts on log(H) and log(W) and model R2, by body site, sex, and year of study
 
Table 6Go shows details of the distributions of counts of large (>=5 mm) naevi. The counts by body site were quite low—indeed the medians for the counts on the arm and leg were 0. Accordingly only the total available counts (over the three sites in 1992 and two sites in 1997) were used for further analysis.


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Table 6 Mean and medians of large (5+ mm) naevus counts, by body site, sex and year of study
 
The correlations of large naevus count with height, weight, and BSA were, as with small naevi, very weak in females, but there were modest associations in males. The correlations in the male data for large naevus frequency with weight and BSA were all approximately 0.32; the correlation of large naevi with height was 0.23 in 1992, and 0.12 in 1997. When regressions were calculated using the log of the large naevus count as the dependent variable, the R2 values in females were again very low; for males, the R2 was 0.06 in 1992 and 0.07 in 1997. Only the coefficient of log(W) was significant, at 1.7 in 1997.

Given the generally weak relationships of naevus counts to height and weight, we carried out further regressions to examine the possibility that these relationships might have been confounded by behavioural effects such as the time spent in the sun or time spent outdoors, and an indicator sun behaviour, number of lifetime sunburns. We carried out further regressions, also including an adjustment for skin type (using the L* measure of skin reflectance). None of the basic conclusions were altered, and the coefficients of height and weight in the regressions were also not materially changed by the inclusion of these additional effects.

As a further investigation of the possibility of confounding by ultraviolet exposure, we examined the relationship between body mass index and the time spent in the sun and time spent outdoors. This was to examine the hypothesis that, for example, those who are relatively obese avoid spending time in the sun or outdoors in general. No such relationships were found. There was, therefore, no evidence in these data that the lack of association between naevus frequency and the anthropometric variables was confounded by differences in ultraviolet exposure.


    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The relationships of naevus counts (N) to height and weight are found to be generally weak in this cohort of adolescents. The strongest relationship was found in males for back naevus counts. There was apparently no gain in using BSA as a regressor directly, as opposed to the height and weight variables. Regression analysis using log(H) and log(W) separately is more flexible, because it allows the possibility of identifying general power relationships of these factors to N. The regression results were broadly consistent with coefficients of 0.5, i.e. with the hypothesis that the number of naevi is simply proportional to BSA. However, there was so much variation between individuals that most of the coefficients were not statistically significant, and their confidence intervals included a wide range of plausible values. The stronger association of back naevi with height, weight and BSA in males is unexplained but may reflect sun-related behavioural differences associated with size in males relative to females. However, we could find no evidence of confounding of our regression by indicators of time spend in the sun or outdoors generally in either sex. These conclusions were unaltered by use of large naevi as the dependent variable in the regressions, although here the data were much sparser. Similarly, adjustment for age and skin type made no difference for either small or large naevi.

Although few investigators have considered the risk associated with anthropometric variables, one recent study24 found significant relationships of melanoma risk to height, weight and BSA. A relationship of height to all causes mortality has also been demonstrated.25 Given that naevi are a risk marker for melanoma, it is, therefore, important to consider their relationship to the physical measurements. In fact, we found in our data that naevus counts are not well predicted by height, weight or estimated BSA. One possible reason for the weak relationship between naevus counts and BSA may be that the square root rule (1) to estimate BSA is in fact not very accurate for all people. Very high accuracy has been claimed for the square root rule,19,26 but it is not clear if such high accuracy would apply in the study population, especially given that the subjects are adolescents. Other reasons for the weak relationship would include measurement error in making the naevus counts, and misattribution of naevi to specific body areas.

With the present state of knowledge, both the number and density of naevi should be considered in principle as equally valid in future studies as markers of the risk of melanoma, and in studies dealing with the natural history of naevi. In fact, as indicated earlier, both number and density of naevi have been used in previous literature, with neither having an obvious advantage. If there are some individuals whose BSA is poorly estimated by rule (1), their naevus density would also be inaccurately estimated. This would argue in favour of the naevus count being chosen as the risk marker, in preference to density. The count is also readily understood by epidemiologists and by the public. On the other hand, the possibility remains that density is the ‘correct’ risk marker. If so, then density should also be retained as a variable in future analyses.


    References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
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