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Keywords:

  • polymorphism;
  • COLIA1;
  • vitamin D receptor;
  • estrogen receptor;
  • bone mineral density;
  • birth weight

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Peak bone mass is an important risk factor for the development of osteoporosis in later life. Previous work has suggested that genetic, intrauterine, and environmental factors all contribute to the regulation of bone mass, but the ways in which they interact with each other to do so remain poorly understood. In this study, we investigated the relationship between peak bone mass and polymorphisms of the vitamin D receptor (VDR), estrogen receptor (ER) α, and collagen type Iα1 (COLIA1) genes in relation to other factors such as birth weight, lifestyle diet, and exercise in a population-based cohort of 216 women and 244 men in their early 20s. Stepwise multiple regression analysis showed that body weight was the strongest predictor of bone mineral density (BMD) in women, accounting for 16.4% of the variance in spine BMD and 8.4% of the variance in femoral neck BMD. Other significant predictors were VDR genotype (3.8%) and carbohydrate intake (1.6%) at the spine and vitamin D intake (3.4%) and ER genotype (3.4%) at the femoral neck. Physical activity was the strongest predictor of BMD in men, accounting for 6.7% of the variance at the spine and 5.1% at the hip. Other significant predictors were body weight (5%) and ER PvuII genotype (2.8%) at the spine and weight (3.4%) and alcohol intake (2%) at the femoral neck. Birth weight was not a significant predictor of BMD at either site but COLIA1 genotype significantly predicted birth weight in women, accounting for 4.3% of the variance. We conclude that peak bone mass is regulated by an overlapping but distinct set of environmental and genetic influences that differ in men and women. However, much of the variance in BMD was unexplained by the variables studied here, which suggests that either most of the genes that regulate BMD remain to be discovered or major environmental influences on BMD exist that have not yet been identified.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

PEAK BONE mass is an important determinant of risk for the development of osteoporosis later in life. Current evidence suggests that genetic and environmental factors contribute significantly to the regulation of peak bone mass in healthy individuals. The importance of genetic factors is illustrated by the results of twin and family studies, which have indicated that heredity accounts for between 50% and 85% of the variance in bone mineral density (BMD), depending on the site examined.(1–4) However, environmental factors such as calcium intake and physical activity also have been shown to influence peak bone mass by modulating bone gain during childhood and adolescence.(5–7) Interest has focused also on the possibility that environmental influences during intrauterine life may influence peak bone mass in the light of recent studies, which documented an association between body weight during infancy and body size and bone mineral content (BMC) in young adults.(8,9) In this study, we investigated the relative contribution of genetic and environmental variables to the regulation of peak bone mass in a population-based cohort of young healthy men and women, focusing on the BsmI polymorphism of the vitamin D receptor gene,(10) the PvuII and XbaI polymorphisms of the estrogen receptor (ER) α gene,(11) and the Sp1 binding site polymorphism of the collagen type Iα1 (COLIA1) gene.(12)

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Study population

The study was conducted on participants of the Young Heart's Project, a longitudinal cohort study of risk factors for coronary heart disease in school children from Northern Ireland. The original 1015 subjects, aged 12-15 years of age, were enrolled by random sampling of schools from throughout Northern Ireland in 1987 as described elsewhere.(13)

At the first study visit, each subject completed interviewer-administered dietary and activity questionnaires and underwent a medical examination and fitness testing. Information relating to parental height and weight, length of gestation, and the number of children in the family was obtained also. Birth weight was obtained from computerized child health system records.

The participants were invited by letter to attend another assessment in 1997 when the mean age of the cohort was 22 years. Four hundred and eighty-nine (48.2%) subjects attended this additional assessment, but BMD scans and/or blood samples for DNA analysis were unavailable for 29 subjects, resulting in a final study population of 460 (45.3% of the original cohort). Previously, we reported(14,15) that socioeconomic position was the main determinant of nonattendance at the 1997 visit, although other factors such as decreased height, increased skinfold thickness, and increased systolic blood pressure also were significantly associated with failure to reattend.

All subjects underwent a physical examination and completed a lifestyle questionnaire, including details of diet, smoking history, alcohol intake, exercise history, and family history of osteoporosis. Nutrient intakes were calculated from the dietary histories using a computerized food analysis program (IUNA WISP; Tinuviel Software, Warrington, UK) and physical activity was assessed using a modification of the Baecke questionnaire,(16) which records work-related activity, sports-related activity, and nonsports leisure activity. A total activity score was obtained from the sum of scores in these three domains to give a total score ranging from 3 (low physical activity) to 15 (high physical activity).

BMD was measured at the femoral neck and lumbar spine using a GE Lunar Expert-XL dual-energy X-ray absorptiometer (GE Lunar, Madison, WI, USA). Genotyping was performed on leukocyte DNA extracted from peripheral venous blood samples using standard techniques. Genotyping for the BsmI polymorphism of the vitamin D receptor (VDR) and PvuII and XbaI polymorphisms of the ER was carried out using polymerase chain reaction—restriction fragment length polymorphism (PCR-RFLP)-based methods as previously described.(17,18) Genotyping for the Sp1 binding site polymorphism of the COLIA1 gene was determined by allelic discrimination using the Taqman system (Applied Biosystems, Foster City, CA, USA) as follows. A 176-bp product was amplified using the primers (forward 5′-GATGTCTAGGTGCTGGAGGTTAGGG-3′ and reverse 5′-CCTTCTCTCCTCCCTCGCC-3′) and the fluorogenic probes (5′ TET-CCCGCCCACATTCCCTGG-3′ and 5′-6-carboxy-fluorescein (FAM)-CCCGCCCCCATTCCCTGG-3′). The reaction mixture of 23 μl/well contained 12.5 μl of Mastermix (Applied Biosystems), 9.25 μl of water, 50 nM of each primer, 50 nM of the FAM-labeled probe, 100 nM of the 6-carboxy-4,7,2′,7′-tetrachloro-fluoroscein (TET)-labeled probe and 0.2 μg of genomic DNA. The cycling parameters were 1 cycle of 95°C for 10 minutes followed by 40 cycles of 95°C for 15 s and 60°C for 1 minute. Genotypes were detected on a PRISM 7200 Sequence Detector (Applied Biosystems). All samples were genotyped blind and 20% of all the samples were repeated.

All participants gave informed consent to being included in the study, which was approved by the Research Ethics Committee of the Queens University of Belfast, Northern Ireland.

Statistical analysis was performed using SPSS, Inc. version 10 (SPSS, Inc., Chicago, IL, USA). The χ2 test was used to test for the Hardy-Weinberg equilibrium and to look for differences between genotype distributions in men and women. The relationship between genotype and continuous variables was assessed by one-way ANOVA. The general linear model (GLM)-ANOVA procedure was used to study the association between genotype and BMD by entering individual genotypes into the model as factors along with relevant covariates such as age, weight, height, nutrient intake, smoking, alcohol, and exercise history. GLM-ANOVA was similarly used to probe for gene-gene interactions by including genotypes from more than one polymorphism in the analysis and building an interaction term into the model. Stepwise regression analysis was used to identify predictors of BMD and birth weight. For this analysis, we created dummy variables for individual genotypes, such that there were two levels for each polymorphism (coded 0 or 1) comprising homozygotes for one allele and the combined group of heterozygotes and homozygotes for the other allele. For VDR, the levels were bb (0) versus Bb and BB (1); for ER PvuII, the levels were pp (0) versus Pp and PP (1); for ER XbaI, the levels were xx (0) versus Xx and XX (1); and for COLIA1, the levels were SS (0) versus Ss and ss (1).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

All individuals were in good health at the time of study and virtually all of the women (97.8%) were menstruating normally. None of the individuals were taking medications known to affect bone metabolism and only 8 subjects reported having a positive family history of osteoporosis. Other relevant characteristics of the study population are summarized in Table 1. As expected, men were taller, heavier, and had a higher birth weight and higher BMD values than women. Dietary intake of all nutrients wassignificantly greater in men as was exercise score. Alcohol intake was also higher and more men were current smokers.

Table Table 1.. Clinical Characteristics of the Populations
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One-way ANOVA analysis showed no significant differences between the various polymorphisms studied and anthropometric characteristics such as birth weight, BMD values, dietary nutrient intakes, alcohol intake, and age once the number of comparisons had been taken into account (data not shown). We similarly found no significant association between genotype and categorical variables such as social class or smoking history (data not shown). On GLM-ANOVA analysis, we found significant associations between VDR BsmI genotype and spine BMD in women and ER PvuII genotype and spine BMD in men. Significant associations were observed between ER PvuII genotype and hip BMD in women (Fig. 1). We also looked for evidence of gene-gene interactions in regulating BMD by entering two or more genotypes into the model simultaneously, but no significant interactions were observed (data not shown).

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Figure FIG. 1. Genotype associations with BMD. BMD measurements were adjusted for age, birth weight, height, weight, smoking, exercise score, vitamin C, alcohol, energy, calcium, protein, total fat, and carbohydrate intakes. Significant differences between genotypes as assessed by GLM-ANOVA are indicated.

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Stepwise linear regression analysis was performed to define the relative contribution of genes and environmental factors to the regulation of BMD. All genotype information was entered into the model, along with dietary, exercise, and lifestyle variables; anthropometric variables; and birth weight. The results of this analysis are shown in Table 2. Weight was the most important predictor of spine BMD in women, although other significant predictors included carbohydrate intake and VDR BsmI genotype. Weight also was the most important predictor of hip BMD in women but other significant predictors included vitamin D intake and ER PvuII alleles. Exercise history was the most important predictor of spine BMD in men, along with ER PvuII alleles, weight, and energy intake. Physical activity also was the strongest predictor of hip BMD in men and other significant predictors included weight and alcohol intake. The results were almost identical when we repeated the regression analysis using dietary, exercise, and lifestyle data collected at the age of 12-15 years (data not shown).

Table Table 2.. Predictors of BMD in Men and Women
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We also investigated predictors of birth weight, entering COLIA1, VDR, and ER genotypes into the model, along with maternal and paternal age, height, and weight; length of gestation; parity; and multiple births. The results of this analysis are summarized in Table 3. Significant predictors of birth weight in women were length of gestation (8.6%), mothers' weight (4.4%), multiplicity (3.7%), COLIA1 genotype (4.3%), and maternal age (2.6%). Significant predictors in men were length of gestation (32%), fathers weight (5.3%), multiplicity (3.9%), and mothers' age (3.1%) and height (2.2%). We also divided subjects into tertiles to look for an interaction between birth weight and genotype in predicting adult BMD as reported by Dennison et al.,(9) but no significant interactions were observed (data not shown).

Table Table 3.. Predictors of Birth Weight in Women and Men
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DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

The risk of developing symptomatic osteoporosis later in life is influenced to a large extent by the levels of peak bone mass attained in early adulthood. Peak bone mass is thought to be determined by an interaction between lifestyle and genetic factors,(19–21) but the relative contribution that these factors make in regulating BMD is incompletely understood.(22) In this study we have assessed the contribution of anthropometric, lifestyle, and genetic factors to the regulation of peak bone mass in a population-based cohort study of young white men and women.

Overall, the regression model explained only 21.8% of the total variance in spine BMD and 15.2% of the variance in femoral neck BMD in women and 17.5% and 10.6%, respectively, of the variance in BMD in men. These findings are in close concordance with the observations made by Rubin et al.(22) in a young female Canadian cohort and suggests that most predictors of peak bone mass remain to be defined.

In agreement with previous work, we found that body weight was one of the most important determinants of BMD at both the spine and the hip.(7,22) In women, weight accounted for 60.2% of the explained variance in BMD at the hip and 75% at the spine.

Although weight also was a significant predictor of BMD at both sites in men, physical activity was by far the most important determinant of BMD in men, accounting for 38.2% of the explained variance at the spine and 48.1% at the hip. The positive association between physical activity and peak BMD in men is consistent with previous data(7) whereas lack of effect in women differs from that reported previously.(22,23) The reason for this discrepancy is unclear but may have been caused by the fact that few women in this cohort actively participated in sports that involved high peak strain (C. E. Neville, M. McGuinness, and C.A.G. Boreham, unpublished data, 2000). In contrast, almost all (91.2%) of the young women studied by Rubin et al.(22) exercised for at least 30 minutes a week.

Of the other environmental variables assessed, vitamin D intake had a positive effect on hip BMD in women (3.4% of total variance; 22.3% of explained variance). In men, energy intake had a positive effect on spine BMD (3% of total variance; 17.1% of explained variance) and alcohol intake had a positive effect on hip BMD (2.1% of total variance; 19.8% of explained variance). The lack of a significant association between COLIA1 genotype and BMD is consistent with previous studies, which have indicated that this polymorphism may be associated with osteoporotic fractures by affecting age-related bone loss and bone fragility, rather than peak bone mass.(19,24–27) The significant association between VDR BsmI and BMD, which we observed here, is in broad agreement with the results of previous studies, which have indicated that allelic variation in VDR contributes to the regulation of peak bone mass.(22,28) In this study, the bb genotype was associated with high BMD, which is consistent with that originally reported by Morrison(29) and other groups(28,30) but differs from that reported in young Canadian women(22) and older women from the Netherlands(31) and Scotland.(17) This supports the view that the BsmI polymorphism in the 3′ region of VDR probably is acting as a marker for a functional polymorphism elsewhere in VDR or in another gene nearby.

The positive association that we observed between the ER PvuII pp genotype and BMD also is consistent with the findings reported by other workers who studied postmenopausal women from Japan(11) and Scotland.(18) However, it should be noted that several other investigators have failed to detect an association between the PvuII and XbaI polymorphisms of the ER and BMD.(32–34) Currently, the mechanisms by which these intronic polymorphisms of the ER gene contribute to regulation of BMD remain unclear.

There is some evidence to suggest that birth weight acts as a predictor of bone mass in early(35) and later life.(36,37) The availability of data on birth weight from individuals who took part in this study allowed us to explore the hypothesis that intrauterine environment influences susceptibility to osteoporosis. In this study we found no significant association between birth weight and BMD, although this is in agreement with the study of Cooper and colleagues who similarly failed to detect such an association in young women from the United Kingdom.(8) Although Cooper reported a positive association between birth weight and BMC, we found no such association once parental height and weight had been taken into account. Other workers have reported an allelic association between VDR genotype and birth weight. We did not detect an association between VDR genotype and birth weight in this study, but we did detect an association between COLIA1 genotype and birth weight.

In summary, our study has revealed that environmental factors, together with polymorphisms in the VDR and ER candidate genes explain ∼18% of the variance in peak bone mass in women and 14% in men. Although it remains possible that environmental variables not assessed in this study also may contribute to the variance in peak bone mass, it seems much more likely that the bulk of the unexplained variance is caused by allelic variation in candidate genes, which have yet to be defined. Further studies also are required to assess more fully the relationship between genetic factors and the determinants of growth and body size. If the pathogenesis of osteoporosis can be traced back to childhood or earlier, then identification of the genes responsible for these effects will have a major impact on the diagnosis, prediction, and prognosis of the disease.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

This study was supported by a grants from the Wellcome Trust (to C.B., L.M., and S.H.R.), a grant from the British Heart Foundation (to C.B. and L.H.), and a cooperative group grant from the MRC (to S.H.R.).

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
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