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J Bone Miner Res. May 2008; 23(5): 644–654.
Published online Feb 4, 2008. doi:  10.1359/JBMR.080105
PMCID: PMC2674539

In Vivo Genome-Wide Expression Study on Human Circulating B Cells Suggests a Novel ESR1 and MAPK3 Network for Postmenopausal Osteoporosis

Abstract

Introduction

Osteoporosis is characterized by low BMD. Studies have shown that B cells may participate in osteoclastogenesis through expression of osteoclast-related factors, such as RANKL, transforming growth factor β (TGFB), and osteoprotegerin (OPG). However, the in vivo significance of B cells in human bone metabolism and osteoporosis is still largely unknown, particularly at the systematic gene expression level.

Materials and Methods

In this study, Affymetrix HG-U133A GeneChip arrays were used to identify genes differentially expressed in B cells between 10 low and 10 high BMD postmenopausal women. Significance of differential expression was tested by t-test and adjusted for multiple testing with the Benjamini and Hochberg (BH) procedure (adjusted p ≤ 0.05).

Results

Twenty-nine genes were downregulated in the low versus high BMD group. These genes were further analyzed using Ingenuity Pathways Analysis (Ingenuity Systems). A network involving estrogen receptor 1 (ESR1) and mitogen activated protein kinase 3 (MAPK3) was identified. Real-time RT-PCR confirmed differential expression of eight genes, including ESR1, MAPK3, methyl CpG binding protein 2 (MECP2), proline-serine-threonine phosphatase interacting protein 1 (PSTPIP1), Scr-like-adaptor (SLA), serine/threonine kinase 11 (STK11), WNK lysine-deficient protein kinase 1 (WNK1), and zinc finger protein 446 (ZNF446).

Conclusions

This is the first in vivo genome-wide expression study on human B cells in relation to osteoporosis. Our results highlight the significance of B cells in the etiology of osteoporosis and suggest a novel mechanism for postmenopausal osteoporosis (i.e., that downregulation of ESR1 and MAPK3 in B cells regulates secretion of factors, leading to increased osteoclastogenesis or decreased osteoblastogenesis).

Key words: osteoporosis, BMD, microarray, B cells, estrogen receptor 1, mitogen activated protein kinase 3

INTRODUCTION

Osteoporosis is a major public health problem and mainly characterized by low BMD.(1) Low BMD results from bone resorption (by osteoclasts) exceeding bone formation (by osteoblasts). It has also been well known that estrogen deficiency increases osteoclastic bone resorption and bone loss in postmenopausal women.(25) The immune system is strongly related to bone metabolism by its interaction with osteoclasts and osteoblasts.(68) Pathological bone resorption has been observed in diseases related to the immune system, such as autoimmune arthritis, periodontitis, Paget's disease, and bone tumors.(9)

As an important cell type in the immune system, B cells may participate in osteoclastogenesis. RANKL binds to its receptor RANK on precursors of osteoclasts and stimulates osteoclastogenesis.(10) Interleukin 7 (IL7) is involved in the growth and differentiation of hematopoietic cells, the stem cells of osteoclasts.(11) Studies have indicated that B cells possibly promote osteoclastogenesis through direct expression of RANKL(12,13) or as a consequence of IL7 stimulation.(14,15) Transforming growth factor β (TGFB) is known to stimulate the proliferation of preosteoblasts, bone collagen synthesis, and osteoclastic apoptosis.(16) An in vitro study also found that peripheral blood B cells inhibit human osteoclastogenesis through secretion of TGFB.(17) Osteoprotegerin (OPG), as a decoy receptor competing with RANK, can bind RANKL and block its effect on osteoclastogenesis.(18) A recent study in both animal model and humans in vivo showed that the amount of OPG produced by B cells regulates osteoclastogenesis and thus BMD.(19)

In addition, B-cell precursors are able to differentiate into osteoclasts in vitro,(20,21) and estrogen deficiency stimulates B lymphopoiesis,(22) which suggests that estrogen deficiency may enhance osteoclastogenesis by increasing the number of B-cell precursors with the potential for osteoclastic differentiation(19) and by stimulating B cells to produce more factors that simulate osteoclastogenesis.

However, the role of B cells in bone metabolism and osteoporosis is still largely unknown, particularly at the systematic gene expression level in humans in vivo. Microarray technology is a powerful tool for studying genome-wide differential gene expression. In our previous study using the microarray approach, we found that expression levels of the chemokine receptor 3 (CCR3), the histidine decarboxylase (HDC), and the glucocorticoid receptor (GCR) genes in blood monocytes may influence risk of osteoporosis.(23)

In this study, we applied microarray technology to freshly isolated B cells from postmenopausal women with low or high BMD to identify differentially expressed genes that may illuminate the functions of B cells in bone metabolism and osteoporosis. This is the first genome-wide expression study on in vivo human B cells relating to the etiology and molecular genetic mechanisms of osteoporosis. A novel estrogen receptor 1 (ESR1) and mitogen activated protein kinase 3 (MAPK3) network in B cells was suggested for the etiology of postmenopausal osteoporosis.

MATERIALS AND METHODS

Subjects

This study was approved by the Institutional Review Board, and all the subjects signed informed-consent documents before entering the project. All the study subjects were whites of European origin recruited from the vicinity of Creighton University in Omaha, NE, USA.

We recruited 20 unrelated postmenopausal white women, 54–60 yr of age, including 10 with low and 10 with high BMD. The inclusion criteria were spine or hip Z-score < −0.84 for the low BMD group (bottom 20% of the age-, sex-, and ethnicity-matched population) and spine or hip Z-score >0.84 for the high BMD group (top 20% of the age-, sex-, and ethnicity-matched population). Postmenopause is defined as the date of the last menses followed by at least 12 mo of no menses. Detailed characteristics of the study subjects are given in Table 1.

Table 1
Characteristics of the study subjects

Seventy milliliters of blood was drawn from each recruited woman. Information such as age, ethnicity, menstrual status, medication history, and disease history was obtained through questionnaire. Exclusion criteria were used to minimize potential effects of any known nongenetic factors on bone metabolism and BMD determination.(24) Please find the detailed exclusion criteria in the Appendix.

BMD measurement

BMD (g/cm2) for the lumbar spine (L1–L4) and total hip (femoral neck, trochanter, and intertrochanteric region) were measured by 4500A DXA scanners (Hologic, Bedford, MA, USA). The machine was calibrated daily. The measurement precision as reflected by the CV was 0.9% and 1.4% for spine and hip BMD, respectively.

Experimental procedures

B-cell isolation:

B-cell isolation from 70 ml whole blood was performed using a positive isolation method with Dynabeads CD19 (Pan B) and DETACHaBEAD CD19 (Dynal Biotech, Lake Success, NY, USA) following the manufacturer's protocols. B-cell purity was assessed by flow cytometry (BD Biosciences, San Jose, CA, USA) with fluorescence-labeled antibodies: PE-CD19 and FITC-CD45. The average purity was 96.3% with <1% deviation.

Total RNA extraction:

Total RNA from B cells was extracted using Qiagen RNeasy Mini Kit (Qiagen, Valencia, CA, USA). Total RNA concentration and integrity were determined by an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). Each RNA sample has an excellent integrity number >9.0 in this study, indicating that RNA degradation caused by processing was minimal and negligible.

Preparation of cRNA and GeneChip hybridization:

For each sample, 4 μg total RNA was used for the production of cRNA. The production of cRNA, hybridization, and scanning of the HG-133A GeneChip were performed according to the manufacturer's protocol (Affymetrix, Santa Clara, CA, USA).

Real-time RT-PCR:

Two-step real-time RT-PCR was used to verify the differentially expressed genes identified from the analyses of chip experiments. The first step is RT for synthesis of cDNA from total RNA and the second step is real-time quantitative PCR.

The RT reaction was performed in a 100-μl reaction volume, containing 10 μl 10× Taqman RT Buffer, 22 μl 25 mM MgCl2, 20 μl dNTPs, 5 μl 50 μM random hexamers, 2 μl RNase inhibitor, 2.5 μl MultiScribe reverse transcriptase, 1 μg total RNA, and water to 100 μl. All the RT reagents were supplied by Taqman Reverse Transcription Reagents (Applied Biosystems, Foster City, CA, USA). Reaction conditions were as follows: 10 min at 25°C, 30 min at 48°C, and 5 min at 95°C.

Multiplex real-time quantitative PCR was performed in a 25-μl reaction volume using standard protocols on an Applied Biosystems 7900HT Fast Real-time PCR System. The procedures were detailed in our previous study.(23)

Data analyses

Differential expression analyses:

Microarray Suite 5.0 (MAS 5.0; Affymetrix) software was used to generate array raw data in CEL files. The CEL files were imported into the R software package (http://www.r-project.org), and the probe level data in CEL files were converted into expression measures and normalized by Robust Multiarray Algorithm (RMA; http://www.bioconductor.org)(25) using the Affy package(26) from Bioconductor (http://www.bioconductor.org/) in R environment.

Afterward, the RMA-transformed data were analyzed by Bioconductor's Multtest package to identify differentially expressed genes between the low and the high BMD groups. In this package, the differential expression was tested by t-statistics. The Benjamini and Hochberg (BH) procedure(27) was used for multiple-testing adjustment, and adjusted p ≤ 0.05 was used as the significant criterion.

Clustering and gene ontology analyses:

According to the similarity of gene expression, the differentially expressed genes were further clustered hierarchically in two dimensions at both the gene and sample levels(28,29) using Cluster (version 2.50) software.(30) To gain an overall picture of potential functions of the differentially expressed genes, we classified the genes according to three organizing principles (biological process, molecular function, and cellular component) of the gene ontology (GO) database (http://www.geneontology.org/) by Onto-Express analysis (http://vortex.cs.wayne.edu/ontoexpress/).

Network and pathway analyses:

The differentially expressed genes were further analyzed using Ingenuity Pathways Analysis (IPA; Ingenuity Systems, www.ingenuity. com). A data set containing Affymetrix probe set identifiers and corresponding BH-adjusted p values was uploaded into the application. Each identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. These genes, called focus genes, were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base. Networks of these focus genes were algorithmically generated based on their connectivity. Using this system, we also performed Canonical Pathway Analysis that identified the pathways from the IPA library of canonical pathways that were most significant to the data set. The significance of the association between the data set and the canonical pathway was measured in two ways: (1) a ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the canonical pathway is displayed; and (2) a Fisher's exact test was used to calculate a p value determining the probability that the association between the genes in the data set and the canonical pathway is explained by chance alone.

Real-time RT-PCR analyses:

The cycle number at which the reaction crossed a predetermined cycle threshold (CT) was identified for each gene, and the expression of each target gene relative to the GAPDH gene was determined using the equation 2−ΔCT, where ΔCT = (CTTarget Gene − CTGAPDH). Based on the relative gene expression, we performed Student's t-tests to validate the differentially expressed genes between the two discordant BMD groups.

RESULTS

Differential expression analyses

We submitted the raw data to the NCBI Gene Expression Omnibus data repository with accession number GSE7429. On average, 36.78 ± 2.03% of the total of 22,283 probe sets in the array were called “present” for our samples based on the analysis with the MAS 5.0 software. We identified 29 genes differentially expressed between the low and high BMD groups after BH adjustment (Table A1). Interestingly, all the 29 genes were downregulated in the low BMD group.

Table A1
Genes Differentially Expressed Between the High and Low BMD Groups

Clustering and GO analyses

Figure 1 shows the results of the 2D clustering analyses of the 29 differentially expressed genes. As shown in Fig. 1, low and high BMD subjects can be largely separated into two clusters. Figure A1 represents the results of the GO analyses. In the “Biological Process” principle, functions of the 29 genes are focused on DNA-dependent regulation of transcription, amino acid phosphorylation, transcription, and signal transduction. In the “Molecular Function” principle, the functions are mainly on protein binding, zinc ion binding, metal ion binding, ATP binding, and transcription factor activity. In the “Cellular Component” principle, we can see that the products of those genes are primarily located on membrane and nucleus.

FIG. 1
2D hierarchical dendrograms clustered both the 29 genes and studied individuals. The horizontal axis shows the clustering of subjects within the two BMD groups (L, low BMD; H, high BMD; B, B cells; numbers, subject codes), and the vertical axis represents ...
FIG. A1
Classifications of the differentially expressed genes according to the GO principles. The number and percentage of the genes in each category are indicated on the left.

Network and pathway analyses

Using IPA to further analyze the 29 genes, a significant network involving ESR1 and MAPK3 genes was constructed (Fig. 2). This network includes 35 genes, and 15 of them were focus genes. Further canonical pathway analysis performed on the 15 focus genes identified 25 relevant canonical pathways. Names of the canonical pathways and the involved genes in each pathway are summarized in Table A2. It is noticeable that the MAPK3 and ESR1 genes are involved in 24 and 2 pathways, respectively. Both ESR1 and MAPK3 are included in estrogen receptor signaling and extracellular signal-regulated kinase (ERK)/MAPK signaling pathways.

FIG. 2
A network diagram constructed by IPA. Shaded genes are focus genes included in the input 29 differentially expressed genes. Direct interactions are solid lines, whereas indirect interactions are represented by dotted lines. The score is a numerical value ...
Table A2
summary of Canonical Pathway and Involved Genes

Real-time RT-PCR analyses

The selection of genes for real-time RT-PCR is based on criteria of assay availability and potential biological interests. From the 29 differentially expressed genes, we selected 14 genes for real-time RT-PCR, which are C7 (complement component 7), CREB5 (cAMP responsive element binding protein 5), DARC (Duffy antigen/chemokine receptor), ESR1 (estrogen receptor 1), KLK3 (kallikrein 3), MAPK3 (mitogen-activated protein kinase 3), MECP2 (methyl CpG binding protein 2), PRG3 (proteoglycan 3), PSTPIP1 (proline-serine-threonine phosphatase interacting protein 1), SLA (Src-like-adaptor), SLC26A3 (solute carrier family 26, member 3), STK11 (serine/threonine kinase 11), WNK1 (WNK lysine deficient protein kinase 1), and ZNF446 (zinc finger protein 446). The results confirm downregulation of eight genes including ESR1, MAPK3, MECP2, PSTPIP1, SLA, STK11, WNK1, and ZNF446 in the low BMD group (Fig. 3). Remarkably, except for ZNF446, all seven other differentially expressed genes verified by real-time RT-PCR are included in the ESR1 and MAPK3 centered gene network (Fig. 2).

FIG. 3
Comparison of real-time RT-PCR expression levels of the eight confirmed genes between the low and the high BMD groups. Gene expression levels were given by 2−Δ CTCT = CT Target Gene − CT GAPDH; the CT data used to determine ...

DISCUSSION

Lymphocytes have proven to be important in bone turnover.(31) However, most studies have been devoted to the function of T lymphocytes on bone metabolism.(32) Only a few studied the role of B cells in bone turnover, but they focused on specific candidate genes. Using Affymetrix microarray technology, this study for the first time systematically measured gene expression of human B cells in vivo to study their role in bone metabolism. Based on the 29 differentially expressed genes, a network composed of 15 focus genes including ESR1 and MAPK3 was constructed (Fig. 2). Further real-time RT-PCR confirmed downregulation of eight genes, including ESR1, MAPK3, MECP2, PSTPIP1, SLA, STK11, WNK1, and ZNF446, in the low BMD group. Our results suggest a potential novel B cell–mediated pathophysiological mechanism for the etiology of osteoporosis, which is that downregulation of ESR1 and MAPK3 in B cells regulates secretion of factors leading to increased osteoclastogenesis or decreased osteoblastogenesis.

Numerous population genetic studies have detected evidence of association between ESR1 polymorphisms and osteoporotic risk.(33) A mutant ESR1 gene was found to result in reduced BMD in both human(34) and mice.(35,36) The relationship between ESR1 and EGF (epidermal growth factor) is suggested in the IPA network constructed from our results (Fig. 2). It was indicated that the expression of the ESR1 gene positively affects the expression of the EGF gene in human cancer cells,(37) and EGF is an important osteogenic growth factor stimulating the differentiation of osteoblasts.(38) The ESR1 gene was also positively correlated with the expression of the IGF1 (insulin-like growth factor 1) gene,(39) another important osteogenic growth factor that enhances function of osteoblasts(40) and prevents osteoblastic apoptosis.(41) Low IGF1 levels induce a BMD decrease in elderly women.(42) In our study, in the low BMD group, low expression of the ESR1 gene in B cells may decrease the secretion of EGF and IGF1 and thus result in low BMD.

MAPK3, also known as ERK1, is a very important signaling molecule. In IPA canonical pathway analyses, it was seen that MAPK3 participates in 24 canonical pathways. Particularly, both ESR1 and MAPK3 are critical factors in the estrogen receptor signaling and the ERK/MAPK signaling pathways. In the estrogen receptor signaling pathway, ESR1 positively regulates the activation of MAPK3,(43,44) and MAPK3 protein increases activation of the nucleus ESR1-estrogen dimmer.(45) As transcription factors, the ESR1-estrogen dimmer and the RNA pol2-transcription factor together stimulate the expression of the IGFBP1 (IGF binding protein 1) gene.(46) IGFBP1 is able to extend the half-life and enhance the biological activity of IGF1.(47) In the ERK/MAPK signaling pathway, conversely, MAPK3 increases activation of ESR1.(48) Thus, the downregulation of MAPK3 may decrease activation of ESR1 and accordingly reduce the expression of bone-forming factors stimulated by MAPK3 per se and by ESR1 in low BMD subjects. In addition, MAPK3 is a stimulating factor in a canonical apoptosis signaling pathway(49) in IPA analyses. The downregulation of the MAPK3 gene may restrain pre-B cell or B cell apoptosis and provide a larger reservoir for osteoclastic differentiation in low BMD women versus high BMD women.

From the IPA network, we notice that ESR1 and MAPK3 (Fig. 2) also indirectly regulate five of the other six real-time RT-PCR–confirmed genes except for ZNF446. Among the six genes, MECP2 was recently found to suppress expression of the RANKL gene in mouse osteoblasts by contributing methylation of the RANKL gene promoter.(50) Hence, the downregulation of MECP2 will increase the RANKL expression and consequently stimulate osteoclastogenesis. Moreover, studies have also found that human MECP2 gene mutations in Rett syndrome reduce bone formation and cause osteoporosis.(51) The downregulation of MECP2 in the low BMD group is consistent with the above evidence. MECP2 is located at Xq28. Interestingly, our previous two whole genome linkage studies on BMD in 4,126, and 1816 subjects, respectively, identified linkage signals at Xq27,(52,53) and the linkage peak marker is only 3 Mb away from the physical location of MECP2 gene.

STK11 mutant mice displayed increased death of mesenchymal cells,(54) which are the stem cells of osteoblasts. The activation of STK11 is also increased by EGF.(55) PSTPIP1 protein mutant human cell lines showed elevated secretion of interleukin 1 β (IL1B) protein,(56) a factor stimulating osteoclatogenesis and bone resorption.(57) SLA mutant mice showed an increment in B-cell quantity,(58) and activation of naïve B cells decreased the expression of SLA mRNA in mice.(59) This evidence suggests that downregulation of the SLA gene may increase B cells with potential of osteoclastic differentiation and also activate the function of B cells contributing to bone loss. In our study, STK11, PSTPIP1, and SLA genes were downregulated in the low BMD group, which confirms results of the previous studies.

As to WNK1 and ZNF446 genes, no direct evidence shows their relationship with B cells, osteoblasts, or osteoclasts. Recently, however, downregulation of WNK1 protein kinase was found to suppress proliferation of neural progenitor cells likely by involving activation of the ERK/MAPK3 signaling pathway.(60) Presumably, the same mechanism exists in B cells. The ZNF446 gene is a novel gene that was recently identified and may act as a transcriptional repressor in the ERK/MAPK3 signaling pathway.(61) As we discussed, a suppressed ERK/MAPK3 signaling pathway decreases activation of ESR1.

Human circulating B cells were studied in this work for their role in bone metabolism. In the human peripheral skeleton, such as the femur, the sinusoid of basic multicellular unit (BMU) proved to be the sole access route for circulating monocytes to enter the bone microenvironment to differentiate into osteoclasts.(62) Similarly, circulating B cells may also move into the human bone microenvironment through the sinusoid of BMU and carry on their role in osteoblastogenesis or osteoclastogenesis.

To obtain a whole picture of the above-discussed connections among the eight genes and the relations between the genes and osteoblastogenesis, osteoclastogenesis, or B cells, we constructed a specific molecular and cytological network centered on ESR1 and MAPK3 (Fig. 4). In summary, our results suggest that ESR1 stimulates secretion of two osteoblastogenesis factors: EGF and IGF1. EGF also activates another osteoblastogenesis factor, STK11. The ESR1 and MAPK3 network increases the expression of IGFBP1 that improves the function of IGF1. The network also stimulates two factors inhibiting osteoclastogenesis, PSTPIP1 and MECP2, and increases the expression of WNK1 and ZNF446, which have positive feedback effects on the ERK/MAPK signaling pathway. In addition, the network stimulates the expression of SLA, which suppresses B lymphopoiesis.

FIG. 4
Interactions of the eight real-time RT-PCR–confirmed differentially expressed genes and their potential effects on osteoblastogenesis and osteoclastogenesis. The dashed circle in B cells represents the ESR1 and MAPK3 centered network. Stimulation ...

In this study, all the high and low BMD women are postmenopausal and had a narrow age span of 54–60 yr. Menopause and age are the two most important factors associated with bone loss and osteoporosis.(63 64) Furthermore, estrogen deficiency resulting from menopause is a principle factor in the cause of osteoporosis in women.(65) Hence we selected postmenopausal women with a narrow age span to minimize possible perturbations from a mixed sample and to investigate significant molecular and cellular factors for postmenopausal osteoporosis. Interestingly, in the two homogenous samples, we found that the ESR1 gene was upregulated in the high BMD group compared with the low BMD group, although both groups are estrogen deficient, and the correlated significant genes have the same expression trends as the ESR1 gene in both groups. Because osteoporosis and BMD variation are both highly genetically determined,(53) we speculate that high BMD postmenopausal women may genetically counteract the estrogen deficiency effect by upregulation of ESR1 gene expression in B cells, which may mitigate the loss of endogenous estrogen by stimulating a cascade of gene expression and result in reduced bone turnover. For low BMD postmenopausal women, on the contrary, estrogen deficiency may lead to downregulation of the ESR1 gene and subsequent downregulation of the relevant genes in B cells, resulting in bone loss.

Actually, we also performed the chip experiments and comparison analyses for B-cell expression between 10 low and 10 high BMD (with the same inclusion and exclusion criteria except for menopause status and age range) premenopausal women but found no significant differentially expressed genes after the multiple-testing adjustment (data not shown). Lack of evident association of those genes identified in postmenopausal women in premenopausal women dose not necessarily mean that those genes do not contribute to premenopausal bone mass variation but may not significant enough to be detected.

Our study initially compares gene expression profiles and provides evidence for the functional difference of in vivo blood B cells between postmenopausal low and high BMD women. We found that a novel ESR1 and MAPK3 centered gene network (Figs. 2 and and4)4) may contribute to the etiology of postmenopausal osteoporosis. The results provide valuable clues for further molecular and cellular studies on the relationship between B cells and osteoporosis.

ACKNOWLEDGMENTS

This research was partially supported by a grant from Sate of Nebraska (LB595), grants from Natural Science Foundation of China (30230210, 30470534, and 30600364) and the Scientific Research Fund of Hunan Provincial Education Department (04B039, 05B037), and NIH Grants R21 AG027110-01A1, R01 GM60402, R01 AR050496, R01 AG026564, P50 AR055081, and K01 AR02170-01A2.

APPENDIX: EXCLUSION CRITERIA

Criteria to exclude nongenetic factors that may cause BMD variation

(1) Serious residuals from cerebral vascular disease

(2) Diabetes mellitus, except for easily controlled, non–insulin-dependent diabetes mellitus

(3) Chronic renal disease manifest by serum creatinine >1.9 mg/dl

(4) Chronic liver diseases or alcoholism

(5) Significant chronic lung disease

(6) Corticosteroid therapy at pharmacologic levels currently or for >6-mo duration at any time

(7) Treatment with anticonvulsant therapy currently or for >6-mo duration at any time

(8) Evidence of other metabolic or inherited bone disease such as hyper- or hypoparathyroidism, Paget's disease, osteomalacia, osteognesis imperfecta, or others

(9) Rheumatoid arthritis or collagen disease

(10) Recent major gastrointestinal disease (within the past year) such as peptic ulcer, malabsorption, chronic ulcerative colitis, regional enteritis, or any significant chronic diarrhea state

(11) Significant disease of any endocrine organ that would affect bone mass

(12) Hyperthyroidism

(13) Any neurological or musculoskeletal condition that would be a nongenetic cause of low bone mass

(14) Any other disease, treatment (including bisphosphonates), or condition (such as hormone replacement therapy) that would be an apparent nongenetic factor underlying the variation of BMD

Additional criteria to exclude diseases/conditions that may lead to gene expression changes of B lymphocytes

We will not exclude people with a diagnosis of idiopathic osteoporosis or those on calcium and/or vitamin D supplements.

Given that B lymphocytes are also an essential component of the immune system, we will adopt the following additional exclusion criteria in order to minimize the effect of diseases or conditions, which may potentially lead to the protein expression changes:

(1) Autoimmune or autoimmune-related diseases: systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, Graves disease, Hashimoto's thyroiditis, myasthenia gravis, Addison's disease, dermatomyositis, Sjogren's syndrome, Reiter's syndrome

(2) Immune-deficiency conditions: AIDS, severe malnutrition, spleenectomy, other conditions that may result in an immune-deficiency state (ataxia-telangiectasia, DiGeorge syndrome, Chediak-Higashi syndrome, job syndrome, leukocyte adhesion defects, panhypogammaglobulinemia, selective deficiency of IgA, combined immunodeficiency disease, Wiscott-Aldrich syndrome, complement deficiencies)

(3) Haemopoietic and lymphoreticular malignancies: leukemias, lymphomas (Hodgkin's disease, non-Hodgkin's disease), myeloma, Waldenstrom's macroglobulinaemia, heavy chain disease, others (leukemic reticuloendotheliosis, mastocytosis, malignant histiocytosis)

(4) Other diseases: viral infection (influenza), allergy (active periods of asthma), chronic obstructive pulmonary disease

Footnotes

The authors state that they have no conflicts of interest.

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