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. Author manuscript; available in PMC: 2009 Feb 22.
Published in final edited form as: Calcif Tissue Int. 2007 May 16;80(5):307–315. doi: 10.1007/s00223-007-9008-2

Relationship of Volumetric Bone Mineral Density and Structural Parameters with ERα Gene Polymorphisms

C Cepollaro 1, F Lauretani 2, A Gozzini 1, L Masi 1, A Falchetti 1, F Monte 1, S Carbonell-Sala 1, A Tanini 1,3, AM Corsi 2, S Bandinelli 4, L Ferrucci 5, ML Brandi 1,3
PMCID: PMC2646089  NIHMSID: NIHMS45577  PMID: 17505773

Abstract

Bone mineral density (BMD) contributes to bone strength, and methods for clinical assessment of bone quality characteristics beyond what can be gathered by BMD are awaited. Peripheral quantitative computed tomography (pQCT) allows for separate assessments of cortical and trabecular bone, providing information on bone geometry. Previous studies examining the relationship between estrogen receptor α (ERα) gene polymorphisms and BMD have been performed in large populations. However, only limited information is available on the possible segregation of ERα gene polymorphisms with bone structural properties. The aim of our study was to evaluate the association of XbaI and PvuII ERα gene polymorphisms with QCT parameters. We studied 900 subjects (541 women, 449 men) participating to the InCHIANTI study. By tibial pQCT we evaluated trabecular volumetric BMD, cortical volumetric BMD, cortical bone area, and cortical thickness (CtTh). Subjects were genotyped for ERα gene PvuII and XbaI polymorphisms. Analysis of variance was used for statistical analysis. Male subjects with PP and XX genotypes had higher geometric parameters, and female subjects with XX and PP genotypes showed higher densitometric parameters than other genotypes; however, the differences did not reach statistical significance. After adjustment for potential confounders, we found a significant (P = 0.002) CtTh difference across PvuII polymorphism in male subjects, with higher CtTh values in PP genotypes with respect to Pp and pp genotypes. These results show a relationship between the presence of the P allele and higher values of CtTh in male subjects, indicating for ERα a role in the control of tibial bone geometry.

Keywords: Cortical thickness, ERα gene polymorphism, Peripheral bone, Quantitative computed tomography, Volumetric bone mineral density

Osteoporosis is a common skeletal disorder characterized by compromised bone strength predisposing the affected individuals to an increased susceptibility to fragility fracture [1]. Bone strength is the result of both bone mineral density (BMD) and bone quality, the latter encompassing a number of factors, such as bone turnover, mineralization, microarchitecture, and geometry. Osteoporosis-related fractures constitute a major health concern both in women and in men, with about 90% of all spine and hip fractures in elderly Caucasian women and 70% in elderly Caucasian men being attributable to osteoporosis [2]. In combination with several environmental factors, such as diet and lifestyle, genetic factors appear to play a relevant role in the regulation of BMD and risk of fracture in both women and men [3].

Among the several candidate genes, those encoding estrogen receptors (ERs) have been evaluated by several investigators in the genetic regulation of BMD. The majority of the studies have analyzed the XbaI and PvuII polymorphisms of the ERα gene, both in women [4–20] and in men [6, 16, 21–27], assessing bone mass by dual-energy X-ray absorptiometry (DXA). However, these studies did not consider structural variables, such as trabecular and cortical tissue characteristics and bone size. As trabecular and cortical bone compartments and bone geometric parameters, including cortical thickness and cortical area, are increasingly recognized as important components of bone strength, even independently on average BMD [28], the potential relationship of the ERα gene with these bone structural indicators should be carefully analyzed.

In a recent meta-analysis published by the GENOMOS group (Genetic Markers for Osteoporosis) involving almost 19,000 individuals, PvuII, XbaI, and TA repeat polymorphisms of the ERα gene appeared to segregate with fragility bone fracture risk, independently of BMD [29]. These results reinforce the hypothesis that the ERα gene influences other bone properties, such as bone turnover, mineralization, bone quality, and bone geometry, all playing an important role in determining fracture risk [29].

The aim of our study was to evaluate the influence of XbaI and PvuII ERα gene polymorphisms on volumetric BMD (vBMD) and on structural parameters assessed using tibial quantitative computed tomography (QCT) in a large and homogeneous population-based sample of Caucasian men and women.

Subjects and Methods

The Population Sample

InCHIANTI is an epidemiological study performed in two Italian towns located in the Chianti countryside: Greve (11,709 inhabitants, rural area) and Bagno a Ripoli (village of Antella, 4,704 inhabitants, just outside the urban area of Florence). The study population consisted of a random sample of the population aged 65 years and older living in the two catchment areas and 30 men and 30 women randomly selected in each decade between 20 and 70 years. A detailed description of the design and data collection methods of InCHIANTI have been previously published [30]. Of the 1,530 subjects originally sampled, 1,453 (94%) agreed to participate in the study. Of these, 612 men and 693 women underwent a peripheral QCT (pQCT) examination (78% of men and 76% of women were older than 65 years). For the present study, we analyzed data from 449 men and 541 women who consented to provide DNA samples for analysis. The study protocol was approved by the INRCA Ethical Committee. All subjects receiveed an extensive description of the purposes and known risks of the study procedures, and all gave their informed consent.

Measures

After the home interview, participants received a medical visit in a dedicated laboratory. The level of physical activity in the year prior to the interview was classified on an ordinal scale based on responses to a standard questionnaire: (1) hardly any physical activity, (2) mostly sitting (occasional walks, easy gardening), (3) light exercise (no sweat) 2–4 hours/week, (4) moderate exercise (sweat) 1–2 hours/week (level 4), (5) moderate exercise >3 hours/week, and (6) intense exercise (at the limits) more than three times/week. According to this classification, we grouped the participants as (1–3) inactive or having light physical activity, (4–5) having moderate physical activity, and (6) having intense activity.

Data on dietary intake were collected by administering the food-frequency questionnaire created for the European Prospective Investigation into Cancer and nutrition (EPIC) study [31]. Although the EPIC questionnaire was originally developed for and validated in middle-aged persons, previous studies [32, 33] suggested that this tool provides good estimates of dietary intake also when administered to the older population. Participants were asked to specify how frequently (weekly, monthly, yearly) each food and beverage was consumed in the last year. Participants were asked to report the quantity of food consumed, using for reference colored photographs with different sizes of portions for the main dishes. Specific software created for EPIC transformed data on food consumption into daily intake of energy, macronutrients, and micronutrients. Alcohol intake was estimated from the EPIC questionnaire and expressed as grams/day. Data on smoking were derived from the interview questionnaire. Recorded fracture was based on clinical history of hip fractures in the last year, and the “year since menopause” (YSM) was obtained during the medical visit.

Standing height and weight were objectively measured in each participant, and body mass index (BMI) was calculated as weight (kg) divided by height (m2).

Laboratory Measures

Blood samples were drawn in the morning after a 12-hour overnight fast and after participants had been sitting for at least 15 minutes using a standardized method in order to avoid red cell hemolysis. Assays of 25(OH)-vitamin D and parathyroid hormone (PTH) were performed on specimens previously stored at −80°C. 25(OH)-vitamin D was measured by radioimmunoassay (DiaSorin, Stillwater, MN) after extraction of samples with acetonitrile. Intra- and interassay coefficients of variation (CV) were 8.1% and 10.2%, respectively. Serum intact PTH levels were measured using a two-site immunora-diometric assay kit (N-tact PTHSP, DiaSorin). The assay uses two affinity-purified polyclonal antibodies, one specific for the amino-terminal 1–34 portion of the PTH molecule and the second specific for the 39–84 sequence of the hormone. The assay sensitivity was 1.2 ng/L. Intra- and interassay CV were <3.0% and 5.5%, respectively. Total testosterone was assayed using commercial radioimmunological kits (Diagnostic Systems Laboratories, Webster, TX). For total testosterone, the minimum detection limit was 0.03 nmol/L; intra-assay and interassay CV for three different concentrations were 9.6%, 8.1%, and 7.8% and 8.6%, 9.1%, and 8.4%, respectively.

Lower Leg pQCT

Lower leg pQCT was performed in all study participants by means of a recent generation device (XCT 2000; Stratec Medizintechnik, Pforzheim, Germany) [34]. Subjects were seated in front of the apparatus with the right leg extended, positioned inside the gantry of the device. The distal end of the tibia (tibiotalar joint cleft), identified using a pQCT longitudinal scout view, was used as an anatomical marker for the identification of measurement sites. The length of the tibia had been previously assessed as the distance between the medial knee joint cleft and the medial malleolus (both identified by manual palpation) while the participant was lying supine. Standard 2.5 mm thick transverse scans were obtained at 4% of the tibial length, where trabecular bone is most abundant, and at 38% of the tibial length, where the cortical shell is usually thicker than 2.5 mm, thus allowing accurate detection of the bone boundaries. The cross-sectional images obtained from the pQCT were analyzed using BonAlyse (Jyvaskyla- Finland) software, a software for processing pQCT scans that automatically identifies bone tissue (cortical and trabecular) and assesses its density and geometry. Different tissues in the analysis were separated according to different density thresholds. In particular, areas with density values >710 mg/cm3 were considered “cortical bone,” while areas with density values of 180–710 mg/cm3 were considered “trabecular bone.” The following bone parameters were derived from the pQCT images:

  • Trabecular vBMD (vBMDt) (mg/cm3): assessed as the average density of the trabecular bone area detected at the 4% site (cortical bone was excluded from the measurement)

  • Cortical vBMD (vBMDc) (mg/cm3): a selective measure of the apparent volumetric density of cortical bone measured at the 38% site, which is a good marker of bone material property

  • Cortical bone area (tCSA) (mm2): assessed as the cross-sectional area of the voxels with a density >710 mg/cm3, measured at the 38% site (a good measure of total cortical bone)

  • Cortical thickness (CtTh) (mm): the average thickness of cortical bone in the slice (the calculation, averaged over 360 directions [increments of 1°], makes no geometric model assumptions as it is performed on the true shape of the bone mass and a valid marker of bone resistance against compression and tensile loads [35]).

  • Calf muscle cross-sectional area (CSMA): evaluated from a transverse scan performed at 66% of the tibia length from the distal tip of the tibia, which is the level of largest outer calf diameters, with little variability across individuals. The precision error of the XCT2000 is below 1% for volumetric trabecular and cortical density and for cortical bone area [36, 37] and between 1% and 3% for composite geometric parameters [37].

Genotyping

Genomic DNA samples were extracted from ethylenediaminetetraacetic acid (EDTA) peripheral blood using a modified salting out procedure from Miller et al. [38].

The investigated single-nucleotide polymorphisms (SNPs) were T → C and A → G transitions previously identified within the ERα gene (also known as estrogen receptor 1, ESR1), 6q25.1. These two polymorphisms, defined by the restriction enzymes as PvuII (dbSNP [database of SNPs]: rs2234693) and XbaI (dbSNP: rs9340799) restriction fragment length polymorphisms (RFLPs) [39], are in intron 1 (IVS1) of ERα, about 400 bp upstream of exon 2 and 46 bp apart. For XbaI and PvuII, “X” and “P” denote the absence of the respective restriction sites (G and C alleles, respectively). The presence of the restriction site for each endonuclease was conventionally indicated with a lower-case letter (“p” or “x,” respectively, for PvuII and XbaI endonucleases), whereas upper-case letters indicated the absence of the restriction site.

Genotyping for the two polymorphisms was performed using 5′ nuclease TaqMan assays, useful for high-throughput sample analysis in large population genotyping studies due to rapid and relatively inexpensive performance and accuracy.

Accuracy of the genotyping results obtained with the TaqMan reactions was demonstrated in experiments involving RFLP analysis or single-base extension sequencing. Genotyping assignments were successfully performed in a set of 40 human DNA samples and validated by comparison with results from agarose gel electrophoresis of digested polymerase chain reaction (PCR) products and from direct DNA sequencing using an ABI Prism 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA) according to the manufacturer's protocol.

Detection of single-base substitutions using a high-affinity DNA analogue known as locked nucleic acid (LNA) [40, 41] for use in allelic discrimination assays was achieved by various methods. LNA nucleotide analogues consist of a 2′-O,4′-C methylene bridge that reduces the flexibility of the ribofuranose ring and locks the LNA structure into a rigid bicyclic formation [42], enhancing discrimination between matched and mismatched hybridization probes of considerably short length.

PCR primers and LNA probes were designed and synthesized by Proligo Primers & Probes (Proligo, Paris, France) upon the published human sequence using the appropriate GenBank entries (accession number NC_000006). PCR primers flanking the SNPs producing a 135 bp and a 76 bp amplicon, respectively, were as follows: sense primer, 5′-TTCTGTGTTGTCCATCAGTTCATCT-3′, and antisense primer, 5′-ACAATTATTTCAGAACCATTAGAGACCAA-3′, for PvuII polymorphism; sense primer, 5′-ATGCT TTGTCTCTGTTTCCCAGA-3′, and antisense primer, 5′-TCAGAACCATTAGAGACCAATGCT-3′, for XbaI polymorphism. Dual-labeled LNA hybridization probes for each polymorphism, complementary to the antisense genomic DNA strand and spanning the transition sites, were as follows: for PvuII polymorphism, probe 1 (synthesized with the 5′ fluorescent reporter dye 6-carboxyfluorescein, FAM), 5′-(FAM)tgtcCcAgcTgTtTta(BHQ1)-3′, specific for the T allele, and probe 2 (synthesized with the 5′ fluorescent reporter dye 2,5,2´,4´, 5´,7´- hexachloro-6-carboxyfluorescein, HEX), 5′-(HEX)tccCagcCgttTtatgc(BHQ1)-3′, specific for the C allele; for XbaI polymorphism probe 1, 5′-(FAM)tggtctAgAgTtGgga(BHQ1)-3′, specific for the A allele, and probe 2, 5′-(HEX)tggtctGgAgtTGgg(BHQ1)-3′, specific for the G allele. LNA nucleotides are denoted in upper case, DNA nucleotides are denoted in lower case, and the LNA nucleotide complementary to the identified SNP is underlined. As fluorescent reporter dyes and nonfluorescent quencher combinations are recommended to eliminate background fluorescence and enhance noise-to-signal ratios, attached to the 3′ ends of the four LNA probes is a nonfluorescent black hole quencher (BHQ1).

Genomic DNA samples were used to optimize the real-time SNP genotyping protocol in conjunction with a no DNA template H2O control.

Real-time PCR was performed using the Stratagene (La Jolla, CA) Mx3000-P Detection System, and data analysis was conducted with the corresponding software interface (version 1.20c).

In a 20 μL reaction volume, 1 × QuantiTect Probe PCR Master Mix (Qiagen, Hilden, Germany), 500 nM of each PCR primer, 100 nM of each LNA probe, and 20–25 ng genomic DNA were added. Real-time PCR cycling conditions were initial incubation step at 95°C for 15 minutes for HotStarTaq DNA polymerase activation and DNA denaturation, and then 42 cycles of 94°C for 40 seconds, 68°C for 35 seconds, and 76°C for 35 seconds for PvuII polymorphism and of 95°C for 15 seconds, 67°C for 30 seconds, and 76°C for 30 seconds for XbaI polymorphism.

The allelic discrimination real-time PCR assay with dual-labeled LNA hybridization fluorogenic probes (Proligo) mirrors the 5′ nuclease assay as outlined previously by Livak [43]. Under competitive conditions, perfectly matched LNA hybridization probes to their target sequence elevate the melting temperature of the LNA probe/DNA duplex, minimizing dissociation and prompting cleavage of the probe by 5′ exonuclease activity of Taq DNA polymerase, and inhibit mismatched probes from hybridizing. Cleavage of the hybridized LNA probe releases the 5′ reporter dye (a fluorophore covalently linked to the 5′ end of the molecule), reducing its ability to be quenched and thus releasing emitted fluorescence directly proportional to the amount of PCR product for each respective allele. Finally, each allele is identified by the development of fluorescence of a unique color generated in sealed amplification tubes.

The subjects were classified conventionally as pp (genotype T/T) and xx (genotype A/A) homozygotes, Pp and Xx heterozygotes (genotypes C/T and G/A, respectively), or PP (genotype C/C) and XX (genotype G/G) homozygotes according to the digestion pattern.

Statistical Analysis

Genotype frequencies were tested for Hardy-Weinberg equilibrium by the χ2 test. All analyses were performed separately in men and women. The difference of the means between genotypes was calculated according to sex, using age-adjusted analysis of variance (ANOVA).

After adjusting for multiple potential confounders (age, YSM, PTH, 25[OH]-D, calcium intake, CSMA, height, and weight), we reported the estimated means of CtTh using ANOVA.

All analyses were performed using the SAS statistical package, version 8.2 (SAS Institute, Cary, NC).

Results

The study sample is described in Table 1. The mean age of the subjects was not significantly different for men (66.6 years) with respect to women (67.8 years). No significant differences were found for BMI and number of previous hip fractures. Among postmenopausal women (87.8%), average YSM was 25.1 (10.6 standard deviation [SD]). Hormone replacement therapy was formerly used only by 35 women and never used by 93.4% of the female participants. A significantly higher number of men with respect to women were physically active and smoked. As expected, men had significantly higher values of tCSA, CtTh, vBMDt, and vBMDc than women.

Table 1.

General characteristics of the InCHIANTI population

Men (n = 449) Women (n = 541) P
Age (years) 66.6 ± 15.1 67.8 ± 15.8 0.21
BMI (kg/m2) 26.9 ± 3.4 27.3 ± 4.6 0.16
Hip fractures (n [%]) 7 (1.6) 9 (1.7) 0.88
Physical activity <0.0001
 Sedentary (n [%]) 38 (8.5) 111 (20.5)
 Light (n [%]) 351 (78.2) 406 (75.0)
 Moderate/high (n [%]) 60 (13.3) 24 (4.5)
Smokers (n [%]) 304 (67.0) 112 (22.5) <0.0001
tCSA (mm2) 331.1 ± 48.2 274.1 ± 55.5 <0.0001
CtTh (mm) 5.3 ± 1.0 4.3 ± 1.1 <0.0001
vBMDt (mg/cm3) 225.8 ± 54.8 203.6 ± 59.4 <0.0001
vBMDc (mg/cm3) 1,027.2 ± 57.9 998.0 ± 75.4 <0.0001
CSMA (cm2) 7,228.7 ± 1,247.1 5,775.6 ± 936.1 <0.0001
XbaI genotype
 XX (n [%]) 80 (17.8) 94 (17.4)
 Xx (n [%]) 216 (48.1) 260 (48.0)
 Xx (n [%]) 153 (34.1) 187 (34.6)
PvuII genotype
 Pp (n [%]) 100 (22.3) 133 (24.6)
 Pp (n [%]) 223 (49.7) 251 (46.4)
 pp (n [%]) 126 (28.0) 157 (29.0)
Calcium intake (mg/day) 892.9 ± 324.9 840.2 ± 338.9 0.008
Serum PTH (pmol/L) 23.4 ± 21.2 24.6 ± 13.3 0.40
Serum vitamin D (nmol/L) 62.1 ± 36.2 49.1 ± 37.2 <0.0001
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Values are expressed as mean ± SD

Calcium intake and serum vitamin D levels were significantly higher in men with respect to women, whereas no significant differences in serum PTH levels were found.

The genotype distribution was found to be in Hardy-Weinberg equilibrium, suggesting that the subjects represented a homogeneous genetic background.

The general characteristics of the male and female study populations on the basis of XbaI and PvuII allelic gene variability are described in Tables 2 and 3. No significant differences in XbaI or PvuII allelic variants for all considered variables were found in men and women (Tables 2 and 3).

Table 2.

Age-adjusted mean of characteristics according to ERα; gene polymorphism in men

Pp Pp Pp P XX Xx Xx P
n (%) 100 (22.3) 223 (49.7) 126 (28) 80 (17.8) 216 (48.1) 153 (34.1)
Age (years) 66.4 ± 15.2 67.9 ± 14.1 64.4 ± 16.4 0.26 67.7 ± 14.6 67.6 ± 14.3 64.6 ± 16.4 0.08
BMI (kg/m2) 26.7 ± 3.4 27.0 ± 34 26.8 ± 3.4 0.78 26.8 ± 3.3 27.0 ± 3.5 26.8 ± 3.3 0.91
Hip fractures (n [%]) 4 (4.0) 2 (0.9) 1 (0.8) 0.07 4 (1.9) 2 (2.5) 1 (0.7) 0.25
Physical activity 0.64 0.71
 Sedentary (n [%]) 6 (6.0) 22 (9.8) 10 (7.9) 20 (9.3) 4 (5.0) 14 (9.1)
 Light (n [%]) 80 (80.0) 171 (76.7) 100 (79.4) 169 (78.2) 65 (81.2) 117 (76.5)
 Moderate/high (n [%]) 14 (14) 30 (13.5) 16 (12.7) 27 (12.5) 11 (13.8) 22 (14.4)
Smokers (n [%]) 59 (59.0) 158 (70.1) 87 (69.0) 0.13 150 (69.4) 46 (57.5) 22 (14.4) 0.07
Alcohol intake (g/day) 22.5 ± 18.8 25.2 ± 18.6 25.2 ± 20.8 0.34 20.4 ± 17.6 26.2 ± 19.5 24.7 ± 19.7 0.23
Calcium intake (mg/day) 913.6 ± 338.8 879.8 ± 331.6 867.4 ± 301.6 0.28 910.8 ± 346.9 895.0 ± 337.9 880.3 ± 194.3 0.49
Testosterone (ng/dL) 115.5 ± 70.3 108.6 ± 49.0 116.5 ± 58.0 0.6 113.2 ± 68.4 107.8 ± 52.7 118.3 ± 55.7 0.17
Serum PTH (pmol/L) 26.3 ± 40.2 22.9 ± 11.9 21.7 ± 9.5 0.13 28.2 ± 44.4 22.5 ± 11.7 20. ± 9.9 0.07
Serum vitamin D (nmol/L) 61.9 ± 40.3 63.2 ± 37.0 60.2 ± 30.9 0.7 60.6 ± 41.3 63.2 ± 36.2 61.3 ± 33.4 0.99
CSMA (cm2) 7,242.6 ± 1,195.3 7,150.8 ± 1,219.8 7,359.0 ± 1,333.9 0.26 7,209.1 ± 1,216.2 7,128.2 ± 1,225.5 7,384.2 ± 1,286.6 0.18
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Values are expressed as mean ± SD

Table 3.

Age-adjusted mean of characteristics according to ERα; gene polymorphism in women

PP Pp Pp P XX Xx xx P
n (%) 133 (24.6) 251 (46.4) 157 (29.0) 94 (17.4) 260 (48.0) 187 (34.6)
Age (years) 67.0 ± 17.2 68.1 ± 15.1 68.1 ± 15.6 0.59 67.6 ± 17.6 67.3 ± 15.8 68.7 ± 14.8 0.47
BMI (kg/m2) 27.2 ± 4.7 27.5 ± 4.7 27.0 ± 4.4 0.67 27.6 ± 4.7 27.2 ± 4.7 27.2 ± 4.4 0.59
YSM 41.2 ± 18.3 43.9 ± 16.7 42.3 ± 17.6 0.68 41.9 ± 17.2 42.8 ± 17.9 43.4 ± 16.7 0.54
Hormone therapy (n [%]) 10 (7.9) 14 (5.6) 11 (7.2) 0.6 7 (8.1) 14 (5.4) 14 (7.7) 0.87
Hip fractures (n [%]) 3 (2.4) 2 (0.8) 4 (2.6) 0.31 3 (3.4) 1 (0.4) 5 82.7) 0.85
Physical activity 0.72 0.7
 Sedentary (n [%]) 30 (22.6) 48 (19.1) 33 (21.0) 22 (19.8) 51 (46.0) 38 (34.2)
 Light (n [%]) 97 (72.9) 193 (76.9) 116 (73.9) 67 (16.5) 199 (49.0) 140 (34.5)
 Moderate/high (n [%]) 6 (4.5) 10 (4.0) 8 (5.1) 5 (20.8) 10 (41.7) 9 (37.5)
Smokers (n [%]) 29 (21.8) 57 (22.8) 36 (23.0) 0.91 23 (24.4) 58 (21.0) 41 (22.0) 0.43
Calcium intake (mg/day) 877.7 ± 343.2 804.1 ± 308.7 854.5 ± 366.6 0.64 890.6 ± 367.8 792.8 ± 283.0 871.3 ± 378.0 0.81
Serum PTH (pmol/L) 23.9 ± 10.6 24.6 ± 13.6 24.3 ± 14.7 0.83 24.1 ± 9.8 23.9 ± 13.5 24.9 ± 14.4 0.54
Serum vitamin D (nmol/L) 50.2 ± 39.9 48.2 ± 35.5 49.4 ± 37.8 0.88 48.9 ± 29.8 48.5 ± 37.0 49.9 ± 40.7 0.78
CSMA (cm2) 5,809.6 ± 892.3 5,784.5 ± 957.3 5,732.3 ± 942.8 0.48 5,793.7 ± 884.6 5,812.2 ± 945.9 5,715.8 ± 949.6 0.4
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Values are expressed as mean ± SD

Age-adjusted pQCT parameters categorized on the basis of XbaI and PvuII polymorphisms are reported in Table 4. Although men with PP and XX genotypes tended to have higher geometric parameters and women with XX and PP ERα gene polymorphisms tended to have higher densitometric parameters with respect to other genotypes, the differences did not reach statistical significance.

Table 4.

Age-adjusted mean of cortical bone area, cortical thickness, trabecular BMD, and cortical BMD according to ERα; gene polymorphisms in men and women

PvuII XbaI
PP Pp Pp P XX Xx xx P
Men
 CtTh (mm) 5.5 ± 1.0 5.4 ± 1.1 5.2 ± 0.8 0.2 5.4 ± 1.1 5.4 ± 1.1 5.3 ± 0.9 0.49
 TCSA (mm2) 361.9 ± 48.9 357.6 ± 44.4 354.6 ± 45.9 0.33 361.6 ± 52.1 356.9 ± 43.7 356.8 ± 45.5 0.54
 vBMDc (mg/cm3) 1,026.9 ± 61.9 1,027.3 ± 51.9 1,027.3 ± 64.8 0.94 1,022.1±58.8 1,027.9 ± 53.6 1,026.0 ± 63.2 0.48
 vBMDt (mg/cm3) 224.5 ± 50.2 227.1 ± 57.3 224.7 ± 54.3 0.46 223.8 ± 48.3 226.7 ± 58.0 225.6 ± 53.7 0.66
Women
 CtTh (mm) 4.3 ± 1.1 4.2 ± 1.0 4.3 ± 1.1 0.58 4.3 ±1.1 4.2 ± 1.0 4.3 ±1.1 0.34
 TCSA (mm2) 254.2 ± 52.4 251.2 ± 46.7 255.1 ± 47.9 0.69 255.3 ± 53.5 250.2± 47.9 256.0 ± 47.6 0.21
 vBMDc (mg/cm3) 1,004.9 ± 76.6 996.8 ± 74.9 994.1 ± 74.2 0.55 1,002.3±79.8 997.7 ± 76.0 996.4± 72.6 0.81
 vBMDt (mg/cm3) 206.8 ± 61.7 205.6 ± 57.9 197.7 ± 59.7 0.34 209.5± 61.5 204.3 ±59.7 199.7± 57.9 0.48
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Values are expressed as mean ± SD

After adjustment for potential confounders (age, YSM, PTH, 25[OH]-D, calcium intake, CSMA, height, and weight), no significant differences in pQCT parameters were found among genotypes in women, even when pre- and postmenopausal women were analyzed separately (data not shown). Conversely, CtTh significantly segregated with PvuII ERα gene polymorphisms in male subjects, with higher CtTh values in PP genotype with respect to Pp and pp genotypes (P = 0.002) (Fig. 1). These findings were confirmed using a multivariate regression analysis testing the association between CtTh and PvuII (men: beta = 0.1549, standard error [SE] = 0.05436, P = 0.0045; women: beta = –0.03123, SE = 0.04515, P = 0.4893).

Fig. 1.

Fig. 1

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Mean values of CtTh according to PvuII ERα gene polymorphisms in men.

Discussion

The main purpose of the present study was to evaluate the influence of ERα gene polymorphisms on bone phenotype in a large and homogeneous population-based sample of Caucasian men and women. Given the central role of estrogens in bone metabolism, genes encoding ERs are certainly important candidates for the determination of the osteoporotic risk and ERα is a frequently investigated gene for possible associations with BMD in both sexes [4–27]. The majority of published data are based on DXA measurements of bone mass, this being the standard method for diagnosis in clinical practice. However, the results are controversial [4–27].

In women, the first study on the relationship between BMD and ERα gene polymorphism showed that P or x homozygosity was negatively associated with BMD values in Japanese postmenopausal women [4]. Subsequent studies in Caucasians either confirmed or found the opposite or no association with ERα genotypes [5–20].

In men, data on the relationship between ERα gene polymorphism and BMD are scant and controversial. Ongphiphadhanakul et al. [6] demonstrated that the presence of the P allele was associated with a higher BMD at the lumbar spine in 81 Thai men aged 20–79 years. A significant association between ERα polymorphism and BMD has been also shown in other studies [16, 21, 24], whereas other authors did not find any significant influence of ERα gene polymorphism on BMD in young Finnish [27] and Korean [23] men. The lack of a relationship between ERα gene polymorphism and BMD has been also reported in the adult male population [14, 19, 26].

Several hypotheses have been postulated to account for these discordant results, including ethnic or environmental differences among populations, differences in the study sample, differences in the study design, differences in statistical analyses, and genotyping errors. A recent collaborative study using standardized genotyping methodology on 18,917 individuals tested the contribution of ERα gene polymorphisms on BMD and fractures [29]. In this analysis, the XbaI and PvuII gene polymorphisms were not associated with BMD but XX conferred a highly significant protection against the overall and vertebral fracture risk in women and in men [29]. Qualitative more than quantitative characteristics of bone might, therefore, account for BMD-independent influences of the ERα gene XbaI polymorphism on fracture risk.

DXA is currently considered the technique of choice to assess bone status in clinical practice for the possibility to predict the fracture risk but also for its accuracy, low radiation exposure, and speed of execution. However, the utility of DXA-derived areal BMD is restricted by the inherent planar nature of the measurements, which does not allow true geometric assessment of bone, which is essential for strength estimation. Moreover, DXA cannot discriminate between trabecular and cortical bone characteristics. QCT overcomes most of these problems but, for the high radiation exposure and the cost of the exam, cannot be considered as an alternative to DXA in clinical practice. pQCT was introduced approximately 25 years ago and has been proposed as a relatively inexpensive method to assess bone mass at the appendicular skeleton (radius, tibia) with very low radiation exposure to patients [44]. pQCT measures the true vBMD, allows for separate assessment of trabecular and cortical bone, and provides an actual assessment of the cross-sectional bone geometry. Thus, pQCT may enhance our understanding of the relationships among bone composition, bone density, and bone strength, offering a comprehensive view of bone structural parameters that affect fracture risk [45].

The access to a large and very well-characterized Italian population that was evaluated with this methodology made it possible to test if ERα genotypes are differently associated with tibial bone density and/or geometric parameters. No significant association was found between ERα gene polymorphisms and cortical or trabecular bone density in both sexes, even though both vBMDt and vBMDc were higher in women with XX and PP genotypes with respect to other groups. Our results are in agreement with those reported by Yamada et al. [14] in a large population-based prospective cohort study of aging and age-related diseases, who did not show any significant association between ERα gene polymorphisms and volumetric trabecular or cortical bone density in men and women, assessed by pQCT at the radius.

As geometric parameters such as cortical area and cortical thickness are increasingly recognized as important components of bone strength [46], differences in bone geometry between the sexes may partly explain the lower rates of fragility fractures in men than in women. Indeed, measurements of cortical bone area and cortical thickness by pQCT showed that males have greater values than size-matched females [47]. It has also been shown that CtTh declines with age in women but not in men, suggesting that this may be one of the mechanisms by which older women experience more fractures than elderly men [35]. A thicker cortical shell might also explain the gender-related differences in stress fracture (especially tibial stress fractures) rates seen in military cadets [48]. Noteworthy is that cortical thickness is 20% lower in postmenopausal women with vertebral fractures than in those without vertebral fractures, despite similar BMD values [49]. The role of cortical thickness in determining bone strength has been recently confirmed by Mayhew et al. [28], who measured with CT the distribution of bone in the mid-femoral neck samples from dead subjects.

To our knowledge, our study is the first that evaluates the influence of ERα gene polymorphisms on the geometric characteristics of bone assessed by pQCT at the tibia in adult women. Suuriniemi et al. [50] investigated the interaction between PvuII polymorphisms and physical activity on the modulation of bone mass and geometry in prepubertal and early pubertal Finnish girls, with no significant differences in geometric characteristics of bone among ERα PvuII genotypes. Similarly, in our female population, no significant differences in tCSA and CtTh among ERα gene polymorphisms were found.

No data are available on the relationships between ERα gene polymorphisms and pQCT-derived geometric parameters in men. In our male population, after adjusting for multiple confounders, we observed a significant (P = 0.002) CtTh difference across PvuII polymorphism, with higher CtTh values in PP with respect to Pp and pp genotypes.

The significant relationship between PvuII polymorphisms and geometric parameters of bone could represent the mechanism by which ERα polymorphisms influence fracture risk independently of BMD [29]. The GENOMOS study [29] described an association between XbaI polymorphism and BMD-independent fracture risk, while in our study PvuII polymorphism significantly influenced geometric parameters of bone and, consequently, bone strength. This discrepancy could be due to different methodological approaches between the studies and differences in sample size and ethnicity.

However, we could not demonstrate a significant association between history of hip fracture and PvuII or XbaI, perhaps because of the relatively low number of men and women who reported hip fracture in our study population. Other limitations of our study that may affect these results are the fact that data relative to hospitalization for hip fractures were collected only in the year before the beginning of the study. Moreover, the cross-sectional nature of the study makes it possible to characterize association but not to establish causality. Thus, the existence of a relationship between ERα gene polymorphisms and geometric parameters needs to be confirmed in longitudinal studies on fragility fracture, which represent the gold standard in the determination of the influence of ERα gene polymorphisms on structural parameters independently of BMD. A limitation of this study is also the lack of data on circulating estrogen levels. Since it has been demonstrated that ERα genotype modulates the relationship between BMD and bioavailable E2 levels in men [26], data on estrogen levels could help us to explain the mechanism by which ERα genotypes influence bone structure and fracture risk in the male InCHIANTI population.

These limitations notwithstanding, our findings do provide novel insight into the possible role of ERα gene polymorphisms in regulating bone structure and suggest that bone geometry could be influenced by PvuII identified genotype in men independently of BMD. These data further highlight the importance of examining not just BMD but also geometric parameters of bone which play an important role in the determination of fracture risk.

Acknowledgments

The InCHIANTI study was supported as a “targeted project” (ICS 110.1/RS97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on Aging (N01-AG-916413, N01-AG-821336, 263 MD 9164 13, and 263 MD 821336). This work was also supported by MIUR 2003 (to M. L. B.), Genetic Markers of Osteoporosis in the Italian Population; by FIRB PNR 2001–2003 (protocol RBNE01C5S2, to M. L. B.), Identification of Genetic Susceptibility to Multifactorial Diseases in the Italian Population; by ISS 2003 SARA project 4AF/F10 (to M. L. B.), Correlation Study between Endocrine Estrogenic Activity and Genetic Polymorphisms; and by the Ente Cassa di Risparmio di Firenze (to M. L. B.). We thank Dr. Cosimo Roberto Russo for assistance on the methodological aspect of performing the pQCT scan. The authors have no conflict of interest.

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