Part of this study has been presented in abstract form at the Dutch Society of Bone and Calcium Metabolism, Papendal, November 1995, and World Congress on Osteoporosis, Amsterdam, The Netherlands, May 1996.
This study investigated the effect of a macrobiotic (vegan-type) diet, low in calcium and vitamin D, consumed in early life, on bone mineral during adolescence. Bone mineral content (BMC) and bone area were measured in 195 adolescents (103 girls, 92 boys) aged 9–15 years, using dual-energy X-ray absorptiometry. Ninety-three adolescents (43 girls, 50 boys) had followed a macrobiotic diet in childhood, and 102 (60 girls, 42 boys) were control subjects. After adjustment for bone area, weight, height, percent body lean, age, and puberty, BMC was significantly lower in macrobiotic subjects, in boys and girls, respectively, at the whole body, −3.4% and −2.5%, spine, −8.5% and −5.0%, femoral neck, −8.0% and −8.2%, midshaft radius, −6.8% and −5.6%, and also in girls, at the trochanter, −5.8% (p < 0.05). No group differences were observed at the wrist. Group differences were not explained by current calcium intake or physical activity. We conclude that the use of a macrobiotic diet in early childhood negatively influences adjusted bone mass at age 9–15 years, observations which may hold important implications for fracture risk in later life.
THE IMPORTANCE OF childhood nutrition in relation to various aspects of health and disease in later life has been recently highlighted,1 but data concerning the role of diet in achieving optimal peak bone mass remain inconclusive.2–11 The rate of bone mineral accretion reaches its maximum during adolescence, a period of rapid growth and development,12–14 and peak bone mass attained subsequently is recognized as a major determinant of fracture risk in later life. Calcium requirements and optimum vitamin D status for skeletal health are currently topics of much debate,15 yet few studies have examined the impact of diet on the growing skeleton. Furthermore, studies investigating the effect of childhood diet on adult bone mass frequently obtain dietary information, retrospectively.16
Ten years ago, our studies of Dutch macrobiotic children demonstrated a stunting of growth between 0 and 8 years,17 marked vitamin D deficiency,18 and very low dietary calcium levels in comparison with the usual Dutch diet.19,20 The macrobiotic diet consists of cereals, pulses, and vegetables, with small additions of seaweeds, fermented foods, nuts, seeds, and seasonal fruits. Fish may be consumed occasionally, but meat and dairy products are usually avoided. This unique population, for whom previous dietary and growth data had been collected, provided the opportunity to investigate the influence of these factors on the development of peak bone mass. Thus the aim of this study was to investigate the effect of a macrobiotic diet, consumed from birth onward for a mean period of 6 years, on bone mass in adolescence.
MATERIALS AND METHODS
Between May and July 1995, a total of 195 children (103 girls, 92 boys) aged 9–15 years, participated in this study. Macrobiotic children were recruited from an existing group of macrobiotic families connected with the Human Nutrition Department, Wageningen Agricultural University. One hundred and nineteen macrobiotic families (239 children) were invited to participate. Sixty families (114 children) agreed to take part, but 10 families failed to keep their appointments or bring all the children who originally agreed to be measured, for reasons of illness (n = 2), holidays (n = 3), or subsequent decision not to take part (n = 5). Thus, 43 girls and 50 boys were measured. Almost all the macrobiotic children had participated in previous studies of macrobiotic diet and growth in 1985/8717,20 and 1993.21 A control group of 60 girls and 42 boys of similar age range was recruited through local schools. All subjects were Caucasian, in good health, and not taking medication known to affect bone or calcium metabolism. Socioeconomic status was determined using Attwood scores, a five-point scale based on occupation and highest level of education attained by both parents. Approval for the study was given by the Ethics Committee of Wageningen Agricultural University, and all subjects and a parent gave written informed consent.
Subjects were weighed, in underwear, to the nearest 0.1 kg using a digital scale (ED-60T, Berkel, Rotterdam, The Netherlands). Standing height was measured, without shoes, to the nearest 0.1 cm using a microtoise. Z scores (standard deviation scores) were calculated for height for age and weight for height, using the median (P50) and standard deviation (SD) of national reference data22 such that: Z score = (observed value − P50 reference)/SD reference. The P50 and SD of the reference were interpolated to each child's exact age. Pubertal stage was determined by one trained investigator, according to the method of Tanner,23 using breast development for girls and pubic hair for boys. One subject refused observer assessment and so a self assessment value was used.
Bone mineral content (BMC, g) and bone area (BA, cm2) were determined using a dual-energy X-ray absorptiometer (DEXA, model DPX-L, Lunar Radiation Corp., Madison, WI, U.S.A.) with software version 1.31. Measurements were made of the total body, spine (L1–L4), left hip at the femoral neck and trochanter, and nondominant radius at the 33% (midshaft) and 10% (distal) sites. One spine scan (macrobiotic group) and one hip scan (control group) were defective and therefore could not be included in data analyses.
A spine phantom provided by the manufacturer was scanned weekly throughout the study period, giving coefficients of variation of 0.65% for L1–L4 BMC and 0.73% for L1–L4 areal bone density (BMD, g/cm2). In vivo precision was assessed using repeat scans of six adults, which gave coefficients of reproducibility for BMD ranging from 0.6% for the total body to 3.2% for the trochanter.
Measurements of lean body mass and fat mass were obtained from the total body DEXA scan. Percentage body lean ([nonbone lean mass/body weight] × 100) was used in data analysis as a measure of body composition.
Current calcium intake (mg/day) was estimated using a validated food frequency questionnaire,24 to which several questions were added to include nondairy sources of calcium important in a macrobiotic diet. The questionnaire reference period was the past month, and food intake was estimated in terms of standardized household portion sizes. Daily calcium intake was computed using values from the 1993 release of the Dutch Food Composition Table.25 Few subjects had regular calcium intake from vitamin and mineral supplements, and this was included in daily calcium intake. Physical activity was assessed by asking each subject how much time they spent on physical activity (sports) both during and outside of school time, and the number of minutes spent in sporting activities per week was calculated.
Data were analyzed using SAS system release 6.09 (SAS Institute Inc., Cary, NC, U.S.A.). To investigate group differences in BMC, regression models were constructed in which BMC was the dependent variable, with BA, weight, height, percent body lean, age, and puberty added simultaneously as covariates, and group as the independent variable of interest. All BMC and BA measurements, and three other continuous variables, weight, height, and percent body lean, were first transformed to natural logarithms and multiplied by 100. Group (coded control = 0, macrobiotic = 1) and stage of puberty were treated as discrete variables. This method of analysis was used in preference to the use of bone mineral density (BMD, g/cm2) or bone mineral areal density (BMAD, g/cm3)26 because no assumptions are made about the relationships between BMC and BA, and potential size-related artifacts are avoided.27 An additional advantage of the logarithmic transformation is in the interpretation of results, since the regression coefficient (β) for the group variable represents the percentage difference in adjusted BMC between macrobiotic and control groups.27
The effects of current calcium intake, physical activity, and first-order interaction terms were investigated by adding each variable to the regression models separately. Analyses were performed on data from boys and girls separately. All regression models were analyzed for outlying or influential data points.
Descriptive statistics for the study subjects are given in Table 1. Age, pubertal stage, and height for age Z scores showed no group differences in either sex, but in boys, weight for height Z score was significantly lower in the macrobiotic group. Socioeconomic status did not differ significantly among groups for girls, boys, or for both genders combined. There was a wide range in calcium intake in both macrobiotic and control subjects, with the group means for macrobiotic subjects being significantly lower than those for control subjects in both genders. Macrobiotic children reported following a macrobiotic diet from birth onward for a period of 6.2 ± 2.9 (mean ± SD) years, in most cases subsequently adopting a vegetarian-type diet. Physical activity level did not differ between groups in either gender but percent body lean was higher in macrobiotic boys and girls than in their control group counterparts.
Table Table 1. SUBJECT CHARACTERISTICS BY SEX AND GROUP
Using data from the total body as an example, Table 2 illustrates in detail regression models used in data analysis. Models are shown in four stages to demonstrate the influence of various predictors on BMC and the resulting group difference after adjustment for these factors. In model (i), BMC of macrobiotic and control groups is compared after making an adjustment only for age. Model (ii) includes bone area as a covariate to adjust for bone size. In model (iii), body size and composition are also adjusted for by adding weight, height, and lean fraction as covariates. In model (iv) an additional adjustment is made for puberty.
Table Table 2. THE INFLUENCE OF ADJUSTMENT FOR SEVERAL COVARIATES ON THE GROUP DIFFERENCE IN BMC AT THE TOTAL BODY
In all four models, both macrobiotic girls and boys have significantly lower adjusted BMC than control subjects (p < 0.05), the percentage difference represented by the β coefficient of the group variable. After adjustment for age alone (model i), the group differences were large, and become considerably less after adjustment for bone area (model ii), demonstrating that part of the group difference is due to variation in bone size. Indeed, in model (i) it would appear that age is acting as a proxy for bone area, since when bone area is added (model ii), it is itself a highly significant predictor of BMC, and age is no longer significant, something that was also observed at the other body sites. At all sites, bone and body size explained by far the greatest proportion of the variance in BMC. However, lean fraction was a significant positive predictor of BMC at both hip sites and forearm sites in boys and at the femoral neck and forearm sites in girls. Puberty was an independent predictor at the total body, trochanter, and radius 10% site in girls and radius 10% site in boys, as was age at the spine in boys.
To facilitate comparison with other studies, raw data is shown in Figs. 1 and 2 as scatterplots of BMC against age, for each body site for boys and girls separately. Since these data cannot be interpreted without information about bone and/or body size, scatterplots of weight against age are included in Fig. 3.
Table 3 shows group differences in fully adjusted BMC (i.e., BMC adjusted for bone area, weight, height, percent body lean, age, and puberty) for each site. Adjusted BMC was lower in macrobiotic subjects in both genders at the total body, spine, femoral neck, and radius 33% site, and also at the trochanter in girls.
Table Table 3. ESTIMATES OF GROUP DIFFERENCES IN FULLY ADJUSTED BMC BY SEX
Neither current calcium intake nor current physical activity level was found to be an independent predictor of adjusted BMC at any site in either gender. In macrobiotic subjects, the reported number of years following a macrobiotic diet did not make a significant contribution to the prediction of adjusted BMC.
In general, first-order interaction terms added to the regression models were not significant. In boys, however, there was an interaction between age and group at the spine (p = 0.02) and a weaker one at the total body (p = 0.10). For these sites, regression analyses were repeated on subgroups of older versus younger boys. Subjects were divided to give approximately equal sample sizes, into subgroups of age 9–11 years and 12–15 years. In the younger boys (n = 46), adjusted BMC was lower in macrobiotic than control boys at the whole body, −5.8% (SE 1.5, p = 0.0006), and at the spine, −12.8% (SE 3.6, p = 0.0009). At age 12–15 years (n = 46) these differences were smaller: −1.2% (SE 1.7, p = 0.47) at the total body and −3.3% (SE 3.4, p = 0.34) at the spine. Student's t-tests showed that the percent group differences in younger and older boys were not significantly different from each other.
The extent to which nutritional status and dietary intake in early life may influence peak bone mass is a question which has not been widely addressed. Since bone responds relatively slowly to physiological and nutritional stimuli, it follows that dietary and lifestyle habits during the years of growth and maturation may be more important than once peak bone mass has been reached. As yet, however, it is unknown whether there are critical periods between birth and adulthood during which these potentially modifiable factors exert greater influence.
The present study investigated the consumption of a macrobiotic diet during the first years of childhood on bone mass in adolescence. Studying this particular population has certain advantages; macrobiotic families voluntarily follow a diet of distinctly different pattern and nutritional constitution yet do not differ from the rest of the Dutch population in terms of genetic background or live under particular environmental circumstances. In addition, the educational level of parents is high, minimizing the likelihood that socioeconomic status will be a confounding factor. In the present study, socioeconomic status was similar in the two groups.
Using data from the complete cohorts of boys and girls, macrobiotic adolescents demonstrated significantly lower bone mass for their bone and body size (bone area, weight, height), percent body lean, age, and stage of puberty than control subjects. Although it may be interesting to compare bone mass of children of the same age, our study and others28–30 have shown that bone and body size explain most of the variation in bone mass, and since in children bone and body size often vary greatly within age groups, it is important to adjust for these factors on an individual basis. Similarly, variation in body composition and puberty are often more important in explaining variation in bone mass than chronological age. In our study, even after adjustment for all these factors, macrobiotic subjects had lower bone mass at almost all sites measured, ranging from about 3% at the whole body to a considerable 8% at the spine (boys) and femoral neck (both genders). This was especially surprising since macrobiotic subjects had shown partial catch-up growth by 1993,21 and in the present study, boys had already reached the median of the Dutch reference growth curve and girls were only slightly below. The lower adjusted BMC in macrobiotic subjects was not explained by physical activity or current calcium intake, nor could we find any other reason for the differences. In the absence of other suggestive evidence, the only plausible explanation left would be the diet of the subjects in childhood. We previously measured dietary intakes of the macrobiotic children and an omnivorous group, and differences were striking; at age 1–3 years, macrobiotic children consumed 300–345 mg of calcium/day, approximately 60% of the Dutch reference intake,31 and 30% of the intake of omnivorous children.18,19 In contrast with the majority of Dutch children, many macrobiotic children did not take vitamin D supplements, and both low dietary intakes and serum levels of vitamin D have been demonstrated irrespective of eating habits.18,19 However, it cannot be concluded that a low calcium intake and limited vitamin D status alone are responsible for the observed differences in adjusted BMC; other aspects of the macrobiotic diet may also contribute. Macrobiotic children were also consuming significantly less energy, protein, fat, riboflavin and vitamin B12, and more fiber, thiamin, and nonhaem iron.19,20 High intakes of dietary fiber have been suggested to negatively influence bone metabolism, both by interfering with calcium absorption32 and causing a reduction in plasma levels and increase in excretion of sex steroid hormones.33 Conversely, a low protein intake may be protective to bone mineral, since high protein diets cause more calcium to be excreted.32 Clearly the situation is complex, and it is likely that the dietary influences on bone development seen in the present study are due to a combination of factors.
Findings from previous studies of Dutch macrobiotic children18,20,34 prompted recommendations of dietary changes, specifically to incorporate more oil, fatty fish, and a daily serving of dairy products into the diet, and to decrease fiber intake for children under 2 years of age.20 For these and other reasons, many macrobiotic families have made adaptations to their diet since 1987. Yet although most children in the present study had followed a more lacto-ovo-vegetarian or even omnivorous diet for up to 8 years, adjusted bone mass in macrobiotic subjects now aged 9–15 years was found to be significantly reduced in comparison with control subjects.
There are few longitudinal studies that have addressed the question of how much dietary intake in early life may influence peak bone mass. An observational study in The Netherlands found no effect of calcium intake during adolescence on areal bone density (BMD, g/cm2) at 27 years of age,2 whereas in Chinese children, mean calcium intake between birth and 5 years positively influenced bone mass at age 5.3 Intervention studies evaluating the effect of calcium or nutritional supplements on bone mineral have also yielded inconsistent results. A milk supplement given for 2 years at ages 5–7 did not appear to affect BMD at ages 20–23 in British subjects,5 and although in Guatemala a positive influence of nutritional supplements given between age 0 and 7 years was observed on bone mineral at age 11–27,6 the authors note that this effect was not independent of body size. Short-term positive effects of dairy7 or calcium4,8,9,11 supplements on increases in BMD have been observed in children between ages 6 and 12, but these effects may be only temporary.10
The data from the present study are in agreement with those from many other cross-sectional studies which suggest that puberty, with its major changes in hormonal levels, body dimensions, and composition, is a critical period for bone mass accumulation. Both our study and others have found pubertal stage to be a significant predictor of bone mineral in multiple regression models which also include measures of body size.28–30,35 Cross-sectional studies using either absorptiometry28,36–39 or quantitative computed tomography40,41 have shown that the greatest increases in areal and volumetric bone density, respectively, occur between Tanner stages 3 and 5. These findings have been supported by longitudinal absorptiometric studies demonstrating that maximum rates of increase in BMC,12,13 BMD,13,14 and BMDvol (an approximation of true bone density)13 occur in females between the ages of 11 and 14 and in males between ages of 14 and 17, corresponding to pubertal stages 3–5.
In the present study, the decrease in group difference in adjusted BMC between younger and older boys seen at the total body and spine suggests that puberty might in part be compensating for the lower adjusted BMC seen in macrobiotic children, at least at these sites. If this were to be the case, it would seem that neither intake of calcium nor other nutrients are limiting factors for normal bone mass accumulation in these children. This may be due to the dietary modifications made by the majority of macrobiotic families, or alternatively that these children, accustomed to a low calcium intake throughout life, have adapted to low dietary calcium levels via increased absorption efficiency. There is evidence that adolescents, even on relatively high calcium intakes, absorb a higher percentage of calcium than adults (32 vs. 21%).42 Furthermore, absorption efficiencies as high as 60% have been found in children with a low calcium intake.43 However, although observations based on the smaller subgroups in our study are interesting, interpretations are made with caution since power calculations prior to the study showed that 100 subjects per group would be necessary to detect differences of 3–5% in adjusted BMC between groups. Conclusions on the influence of puberty cannot be drawn from these cross-sectional data, and only future follow-up studies will show whether the older macrobiotic boys represent a special subgroup or whether any catch-up in BMC takes place during the later stages of puberty.
In summary, over the age range of 9–15 years, children who previously followed a macrobiotic diet had a lower relative bone mass than control subjects, especially at the spine and femoral neck. If the differences in bone mass persist until skeletal maturation, they could have important future clinical implications since a low bone mass is a contributing factor to osteoporotic fracture risk in later life.44 Although it is not possible to specify which nutrients are responsible for the observed differences in bone mass, the present data support the recommendations made in 1987,20 particularly the addition of dairy products (as a source of calcium) and fatty fish (as a source of vitamins D and B12) to macrobiotic and other vegan-type diets in children. However, to determine the full impact of maturation and whether a macrobiotic diet in early life has a persistent adverse affect on peak bone mass longitudinal follow-up measurements of these children are required.
We thank all the subjects who participated in this study and Jan Burema for assistance with statistical analysis. This study was supported by grant no. 28–1052-1 from the Dutch Praeventiefonds.