Extract
Note: Please read the complete
full text with Figures and Tables at
Introduction
Osteoporosis is characterized by a decrease in bone mass
as well as a deterioration of the bone architecture resulting
in an increased risk of fracture. Osteoporosis is multifactorial,
and is dependent on environmental and genetic factors. Bone
mineral density (BMD) achieved in early adulthood (peak
bone mass) is a major predictor of osteoporotic fracture risk.
Although environmental factors such as nutrition and
physical activity influence the attainment of peak BMD, there is a
strong evidence for a major genetic contribution to normal
variation in peak BMD[1_3].
Greater body weight is associated with a reduced risk of
osteoporotic vertebral and hip fractures, but the mechanism
of this effect is not well understood. Weight is the major
anthropomorphic determinant of BMD[4,5]. Body fat mass
(BFM) has been proposed as a better predictor of BMD than
body weight and lean mass[6]. High BFM is associated with
high leptin levels[7]. In contrast to leptin, adiponectin is
negatively correlated with body mass index (BMI), BFM,
and fasting insulin concentrations and calculated insulin
resistance[8,9]. Several genetic studies have observed
evidence of association between adiponectin gene
polymorphisms and features of the metabolism syndrome, such as
obesity and insulin resistance[10,11]. However, clinical
studies attempting to associate plasma adiponectin levels with
bone mass have been inconclusive. Kontogianni et
al[12] found that circulating adiponectin levels did not exert any
effect on BMD in 80 perimenopausal healthy women. In an
Estonia study, however, significant negative relationships
were observed between plasma adiponectin and total BMD
and lumbar spine BMD values in 38 perimenopausal women.
Adiponectin had a significant negative association with
total BMD and lumbar spine BMD independent of the
influence that other measured body compositional, hormonal or
physical performance factors may exert on
BMD[13], therefore, more studies need to be done in this area.
Peroxisome proliferator-activated receptor-γ
coactivator-1 (PPARGC1) is a co-activator of PPAR-γ
and other nuclear hormone receptors, and plays an important role in energy
homeostasis. Human PPARGC1 was mapped to chromosome
4p15.1[14]. This chromosomal region has been associated
with abdominal subcutaneous fat in the Quebec Family
Study[15]. Subsequently, Esterbauer
et al[16] investigated associations of 2 polymorphisms (Gly482Ser and +2962A/G)
identified in PPARGC1 transcripts with obesity indices in
591 middle-aged men and 467 middle-aged women of a
cross-sectional Austrian population. The results showed that the
haplotypes were significantly associated with BMI and total
body fat in women, but not in men. Perhaps the association
between bone mass and body weight will be demonstrated
by way of investigating adiponectin and PPARGC1, however,
so far, no published report has investigated the relationship
between bone mass and single nucleotide polymorphisms
(SNP) in adiponectin and PPARGC1 genes. In this study, we
performed population- and family-based association
studies of adiponectin and PPARGC1 to test whether SNP in
adiponectin and PPARGC1 were associated with BMD
variation in the spine and hip in our subjects of healthy Chinese
Han men and women.
Materials and methods
Subject population The study was approved by the
Ethnic Committee of the Shanghai Jiao Tong University
Affiliated Sixth People's Hospital (Shanghai, China). All of the
subjects involved in the study were collected by the
department of osteoporosis from a local population of Shanghai
City located on the middle-east coast of China and signed
informed consent documents before entering the project.
We recruited 401 nuclear families composed of both parents
and at least 1 healthy female child whose age was largely
between 20_40 years, with a total of 1260 individuals. All the
study subjects belonged to the Chinese Han ethnic group,
and all the children were daughters. The average family size
was 3.14, in which 348, 50, 2, and 1 families had 1, 2, 3, and 4
children, respectively. For each study subject, we also
collected information on age, sex, medical history, family history,
marital status, menses history, obstetrical history, physical
activity, alcohol use, diet, smoking history,
etc. The recruiting daughters were healthy. The exclusion criteria was
adopted as previously reported[17,18].
BMD measurements BMD
(g/cm2) of the anteroposterior lumber spine 1_4 (L1_4) and left proximal femur
including total hip and femoral neck were measured by
dual-energy X-ray absorptiometry (DXA) on a Hologic QDR 2000
(Hologic Inc, Bedford, MA, USA). The DXA scanner was
on fan-beam mode. The machine was calibrated daily. The
coefficient of variability (CV) for BMD and BS values was
obtained from 5 repeated measurements on 7 individuals.
CV values of the DXA measurements in the spine, total hip
and femoral neck were: for BMD, 0.9%, 0.8%, and 1.93%,
respectively[18,19]. Height and weight were measured using
standardized equipment.
SNP genotyping DNA was isolated from peripheral blood
leukocytes using conventional methods. The 3 SNP in the
PPARGC1 gene (EMBL AF106698) were examined by the
PCR-restriction fragment length polymorphism (RFLP) as
described previously[20,21]. DNA fragments containing each
SNP (163 bp for SNP Thr394Thr, 452 bp for SNP Gly482Ser
and 343 bp for SNP Thr612Met) were amplified by PCR from
genomic DNA. The sequences of the primers to detect
Gly482Ser polymorphism in exon 8 were
5'-TGAGAGAGAC-TTTGGAGGCA-3' and
5'-GGAATATGGTGATCGGGAAC-3'. The sequences of the primers to detect Thr394Thr
(ACG® ACA) polymorphism in exon 8 were
5'-GCCAGTCAA-TTAATTCCAAACC-3' containing 1 nucleotide mismatch
(underlined), which made it possible to use the restriction
enzyme MspI and 5'-TTGGAGCTGTTTTCTTGTGC-3'. The
Thr612Met variation in exon 9 was amplified with primers
5'-AGTGCCGATAAACTTGGG-3' and 5'-TTCCTCGTAGCTG-TCATACC-3'. PCR was performed on 30 ng DNA in 20 µL
containing Tris-HCl 10 mmol/L (pH 8.3), KCl 50 mmol/L,
MgCl2 1.5 mmol/L, each dNTP 0.2 mmol/L, forward and reverse
primers 0.4 µmol/L, Taq polymerase 0.065 U/µL (Promega,
Madison, WI, USA) for 30 cycles (60 s at 95 °C, 45 s at 55 °C,
and 45 s at 72 °C) in an Eppendorf thermal cycler (Eppendorf,
Hamburg, Germany). PCR products were digested 2 h at
37 °C with HpaII for the Gly482Ser,
MspI for Thr394Thr, and PaeI for Thr612Met. All restriction enzyme digests were
separated on 2.5% agarose gels. The PCR product
containing Thr394Thr SNP was digested with
MspI and shown for the AA (163 bp), GA (163 bp, 142 bp and 21bp), and GG (142
bp and 21 bp) genotypes. The PCR product containing
Gly482Ser SNP was digested with HpaII and shown for the
AA (452 bp), GA (452 bp, 310 bp, and 142 bp), and GG (310 bp
and 142 bp) genotypes. The PCR product containing
Thr612Met SNP was digested with PaeI and shown for the
CC (343 bp), TC (343 bp, 234 bp, and 109 bp), and TT (234 bp
and 109 bp) genotypes.
The genotypes in adiponectin were determined at
positions 45 and 276, relative to the translation start site
(corres-ponding to position 71 and 302 of GenBank NM_004797) by
PCR-RFLP. DNA fragments (456 bp) containing 2 SNP (T45G
and G276T) were amplified by PCR from genomic DNA using
primers 5'-CTGAGATGGACGGAGT-CCTTT-3' and 5'-CCAAATCACTTCAGGTTGCTT-3'. PCR was performed on
30 ng DNA in 20 µL containing Tris-HCl 10 mmol/L, pH 8.3,
KCl 50 mmol/L, MgCl2 1.5 mmol/L, each dNTP 0.2 mmol/L,
forward and reverse primers 0.4 µmol/L,
Taq polymerase
0.065 U/µL (Promega, USA) for 30 cycles (60 s at 95 °C, 45 s
at 58 °C, 45 s at 72 °C) in an MJ Research thermal cycler. PCR
products were digested overnight at 37 °C with
AvaI for T45G SNP and BsmI for G276T SNP, according to the
manu-facturer's instructions, and electrophoresed on 2.5%
agarose gels. T45G genotypes were observed: the TT genotype
produced a 456 bp fragment, the GG genotype produced 313
bp and 143 bp fragments, and the TG genotype produced
143 bp, 313 bp and 456 bp fragments. The G276T GG
genotype produced a 456 bp fragment, the TT genotype produced
82 bp and 374 bp fragments, and the GT genotype produced
82 bp, 374 bp, and 456 bp fragments.
Haplotype assignment The SimWalk2 program estimated
the most probable set of fully typed maternal and paternal
haplotypes of the Gly482Ser, Thr612Met and Thr394Thr
markers in the PPARGC1 gene, and the G276T and T45G
markers in the adiponectin gene for each individual in the nuclear
families[22]. The frequencies of genotypes and haplotypes
were calculated with the unrelated parents of nuclear families.
The significant level of linkage disequilibrium (LD) between
the markers of the same gene was assessed based on the
observed haplotype and allele frequencies using the
HAPLOXT program[23]. LD was expressed as "D'" between
all pairs of biallelic loci.
Statistical analysis Allele frequencies were estimated
by gene counting. The Hardy-Weinberg equilibrium was
tested by a c2 goodness of fit statistic. In this study, the
values of BMD in the spine and hip did not significantly
deviate from normal distribution. In all statistical analyses,
raw bone phenotypic values were adjusted by age and weight
as covariates. Different samples were used for the
population-based association test and the family-based test
(quantitative trait locus transmission disequilibrium test,
QTDT). Because our premenopausal women consisted of
sibling data, 1 sister from each of the 401 families was
randomly selected to generate an unrelated sample for testing
the population-based association hypothesis. To test for
the association between SNP genotypes and haplotypes and
spine and hip BMD variations, ANOVA was performed for
premenopausal women, postmenopausal women, and men
separately, using SPSS version 11.5 (SPSS, Inc, Chicago, IL,
USA). The proportion of BMD variations explained by each
SNP was estimated by the ANOVA
r2 value.
In premenopausal women, population-based methods
such as ANOVA may produce false positive results in
admixed or stratified populations. Therefore, we also performed
a family-based association test using QTDT. The QTDT
program, using the orthogonal model, was used to test for
population stratification, linkage, and within-family
association between SNP in the 2 genes and BMD phenotypes. The
QTDT software package is available on the internet
(http://www.sph.umich.edu/csg/abecasis/QTDT/). This method, as
implemented in the QTDT software[24,25], extends the
trio-based TDT to quantitative trait data and uses genotype data
from available siblings and parents. Because in our nuclear
families all of the children were daughters, and the effects of
parents' phenotypes were excluded in the QTDT, sex was
not used to as a covariate to adjust the daughters' bone
phenotypes variations[26]. Of course, raw BMD values were
adjusted by age and weight as covariates. Because false
positive results might be generated in multiple tests as in the
present study, to assess the reliability of the results,
permutations (1000 simulations) were performed to generate the
empirical P values[27].
P<0.05 was considered significant for all the analyses.
Results
Allele frequencies and LD analysis There were 401
nuclear families with 1260 individuals in the study, including
802 parents and 458 daughters. The basic characteristics of
the study subjects are shown in Table 1. All parental
genotypes were obtained, and the bone phenotypic values of 383
postmenopausal women and 389 men were obtained and
excluded to the secondary causes of
osteoporosis.
A total of 1260 subjects were genotyped in the 2 SNP of
adiponectin and 3 SNP of PPARGC1. The genotype and
allele frequencies of the 2 markers and haplotypes in
adiponectin are presented in Table 2. T45G genotypes of
adiponectin were in the Hardy-Weinberg equilibrium, while
the G276T genotypes were not. The LD tests showed that D'
was 0.87 between the 2 markers of adiponectin. The
genotype and allele frequencies of the 3 markers and haplotypes
in PPARGC1 are presented in Table 3. The genotypes of 3
SNP in PPARGC1 were all in the Hardy-Weinberg equilibrium.
Substantial LD existed between all pairs of genotyped
markers (0.912≤D'≤0.962).
SNP association analyses with BMD Table 4
summarizes the results of the QTDT analysis. There were 324, 228,
269, 299, and 286 informative nuclear families for the TDT
analysis in the Gly482Ser, Thr612Met, Thr394Thr, T45G, and
G276T SNP, respectively. No population stratification was
found for the each SNP in the PPARGC1 and adiponectin
genes in the spine and hip BMD in premenopausal women.
Significant within-family association was found between the
Thr394Thr polymorphism in the PPGAGC1 gene and femoral
neck BMD (P=0.026). Subsequent permutations were in
agreement with this significant within-family association
result (P=0.016). However, no significant within-family
association was detected between each SNP in the adiponectin
gene and BMD at any sites. Moreover, no significant
results for linkage between every SNP of each gene and BMD
in the spine and hip was observed, using tests of linkage
and tests of linkage while modeling association (data not
shown). Although within-family association between
Thr394Thr SNP and femoral neck BMD is significant, this
SNP only accounts for 0.70% of the variation in femoral neck
BMD in 401 unrelated daughters randomly selecting from
each of the 401 families as measured by the ANOVA
r2 value. In addition, no significant association was detected between
each SNP in the 2 genes and BMD in the spine or hip in
postmenopausal women or men (Tables 5, 6).
Haplotype association analyses with BMD We further
observed the results of haplotype association analyses with
BMD. Haplotypes were inferred from PPARGC1 SNP (Gly482Ser, Thr612Met, and Thr394Thr) and adiponectin SNP
(T45G and G276T), respectively. On the basis of these
polymorphisms, we found 4 and 7 different haplotypes in
the adiponectin and PPARGC1 genes presented in our
population, respectively. We identified 4 common
haplo-types with >5% frequency in the PPARGC1 gene, and
together these haplotypes accounted for 98.6% of the
geno-typed chromosomes. The frequencies of haplotypes of 2
SNP in the adiponectin gene were T-T for 44.9%, T-G for
29.2%, G-T for 26.2%, and G-G for 1.0% (Tables 2, 3).
In the group of premenopausal women, postmenopausal
women, and men, no significant differences were found in
BMD in any sites in those with two copies of the most
common haplotype 4 (A-C-G) of PPARGC1 compared with those
with no or 1 copy of the haplotype. With adiponectin
haplotypes, haplotype 2 (T-T) was associated with lumbar
spine BMD in postmenopausal women (P=0.019), and
subjects carrying 1 copy of haplotype 2 had higher lumbar spine
BMD than those with 2 copies of haplotype 2
(P=0.007; Table 7).
Discussion
In this study, we investigated 2 of the most commonly
studied SNP (G276T and T45G) in the adiponectin gene and
3 SNP (Thr394Thr, Gly482Ser, and Thr612Met) in the
PPARGC1 gene, and examined the association between SNP
and spine and hip BMD variations in our large cohort of
healthy Chinese Han men and women. There was no
evidence of association between BMD and adiponectin SNP in
both sexes. However, significant within-family association
was detected for the Thr394Thr polymorphism in the
PPARGC1 gene with peak BMD in the femoral neck in
premenopausal women. Furthermore, population stratification
was not detected for the Thr394Thr polymorphism in the
spine and hip BMD. The QTDT examined the transmission
of alleles from parents to offspring. The within-family
association test can eliminate the effect of admixed and stratified
population. Although the QTDT is less powerful for the
detection of association than the population-based
association test[28], we used a large sample size of 401 Chinese nuclear
families, and obtained 269 informative nuclear families for
the TDT analysis at Thr394Thr SNP. Therefore, the
evidence of tests of within-family association should be more
valuable due be the robustness of the TDT approach.
Furthermore, because false positive results might be
generated in multiple tests as in the present study, we performed
1000 permutations to eliminate false positive results.
Subsequent permutations analyses confirmed this significant
within-family association result (P=0.016). However, this
SNP was not associated with BMD in the spine and hip in
postmenopausal women and men. As a result, our findings
indicate that Thr394Thr SNP in PPARGC1 is of importance
for the attainment of peak BMD, but not for age-dependant
bone loss. Indeed, the attainment and maintenance of peak
bone mass and the rate of bone loss at menopause or old
period was influenced by different genetic
factors[29]. Using population-based methods of analyses, we found that
Thr394Thr SNP explained a very small proportion of the
variation (0.7%) in femoral neck BMD in premenopausal women.
Meanwhile, haplotypes constructed from the 3 SNP (Thr394Thr, Gly482Ser, and Thr612Met) in PPARGC1 showed
no significant association with BMD in both sexes.
Therefore, despite significant evidence of within-family
association between Thr394Thr SNP and femoral neck BMD,
SNP genotypes in the PPARGC1 is not a major contributor to
the observed variability in BMD in Chinese women and men.
We found that genotype frequencies at both the Thr394Thr and Gly482Ser polymorphic sites were in
agreement with the Hardy-Weinberg equilibrium, and the 2
polymorphisms showed a highly significant standardized
pair-wise LD (D'=0.912). Genotype frequencies at both Thr394Thr
and Gly482Ser polymorphisms in our population were
similar with Hara et al, who demonstrated that the Thr394Thr
and Gly482Ser polymorphisms were in strong, but not in
complete LD (D'=0.86) in a Japanese study containing 537 type 1
diabetic patients and 417 nondiabetic
subjects[20]. Previous studies found that the amino acid exchange at Gly482Ser
was conservative and did not create or eliminate protein
motifs known to be functional, and the Thr394Thr
polymorphism in exon 8 was G-to-A substitution and did not result in
amino acid exchange[30]. We observed a significantly
positive association between weight and BMD in present
Chinese women and men. Several studies showed significant
relationships between the Thr394Thr and Gly482Ser
polymorphisms and haplotypes with weight, BMI and
obesity[15,16], however, we failed to find significant relationships
between every SNP and haplotypes in the PPARGC1 gene and
height, weight, and BMI in the within-family association test
(through QTDT), or in the 401 unrelated children (through
ANOVA, data not shown). Furthermore, for tests of
within-family association, BMD values were adjusted for age and
weight using QTDT. Recent studies have identified rodent
quantitative trait locus associated with increased BMD in
the mouse gene encoding
12/15-lipoxygenase[31], the enzyme that converts linolein acid and arachidonic acid into
endogenous ligands for the
PPARγ[32,33]. Several studies have
shown that myoblastic cell lines can be converted to
adipocytes through the expression of PPARγ, and ligand
activation of PPARγ derives the differentiation of multiprotein
mesenchymal progenitor cells towards adipocytes over
osteoblasts[34]. Akune et
al[35] further found the relationship
between osteogenesis and adipogenesis using cells and
animal deficient in PPARg expression. The results showed
that homozygous PPARg-deficient ES cells failed to
differentiate into adipocytes, but spontaneously differentiated into
osteoblasts; PPARγ haploinsufficiency enhanced
osteoblas-togenesis in vitro and increased bone mass in mice
in vivo. In addition, an exonic SNP in the
PPARγ gene is associated with total body BMD in Japanese postmenopausal
women[36]. Therefore, PPARGC1 is a co-activator of
PPAR-γ and other nuclear hormone receptors. Taken together with the results
in this study, these findings support the involvement of
Thr394Thr SNP in PPARGC1 gene in the attainment of peak
BMD, perhaps through a PPARγ-dependent pathway.
Our results showed no significant association between 2
SNP in the adiponectin gene, spine and femoral neck BMD
variations in Chinese women and men using the
population-based analyses and the powerful
QTDT. Meanwhile, as with SNP in the PPARGC1 gene, we failed to find significant
associations between each SNP and haplotypes in the
adipo-nectin gene, height, weight and BMI in the within-family
association test (through QTDT) or in 401 unrelated
daughters (through ANOVA). Our findings are agreement with
Takahashi et al, who found no such relationship with the
same SNP in a Japanese study[37]. We found that the most
common haplotype 2 (T-T) in the adiponectin gene was
associated with lumbar spine BMD in postmenopausal women,
and containing one copy of T-T haplotype in adiponectin,
had higher lumbar spine BMD compared with 2 copies of the
T-T haplotype. However, the biology of this association
between haplotype 2 (T-T) and BMD in the spine in
postmenopausal Chinese women has been unclear. The T45G
polymorphism is a silent T to G substitution in exon 2, and
G276T polymorphism is a G to T substitution in intron 2.
Menzaghi et al[10] observed a significant association between
circulating adiponectin levels and T45G and G276T
polymorphisms in 413 nondiabetic individuals. Although plasma
adiponectin concentrations are reported to be negatively
associated with obesity and
diabetes[12,38], the relationship between circulating adiponectin levels and bone mass have
been contrary[12,13]. A recent study has demonstrated that
adiponectin increases bone mass by suppressing
osteo-clastogenesis and possibly activating
osteogenesis[39]. These findings were also confirmed by Luo
et al[40] who observed that adiponectin induced human osteoblast proliferation and
differentiation, and the proliferation response was mediated
by the adiponectin receptor (AdipoR)/c-jun N-terminal
kinase pathway, while the differentiation response is
mediated via the AdipoR/p38 pathway. These findings suggest
that osteoblasts are the direct targets of adiponectin.
Nevertheless, our present study demonstrated that genetic
variations in the adiponectin gene did not influence peak
BMD variation in Chinese women. Therefore, future more
functional SNP in this gene need to be investigated.
Because all nuclear families in this study consisted of
only 2 generations, and 53 nuclear families containing 62 sib
pairs are informative for the linkage analyses, no linkage for
SNP in the adiponectin and PPARGC1 genes with BMD in
the spine and hip was detected.
To our knowledge, this is the first study to investigate
the possible influence of SNP and haplotypes in the
adiponectin and PPARGC1 genes of BMD in the spine and
hip. Although previous studies have demonstrated that SNP
and haplotypes in the adiponectin and PPARGC1 genes were
associated with obesity and metabolism syndrome, these
studied SNP in the adiponectin and PPARGC1 genes are not
major risk factors for low peak BMD and osteoporosis in the
present study. Our study has several limitations due to the
few SNP studies published, and no functional study of
polymorphism. Therefore, our results should be interpreted
cautiously, and future functional SNP in the promoter region
or code for amino acid sequence change are needed for
better understanding of the role of the 2 genes in the bone mass
in the Chinese population. Of course, our study also has
several advantages: subjects were selected from a relatively
large sample size of nuclear families, and we performed the
tests of population-based association and family-based
association, and applied haplotype analysis. In addition,
considering the false positive results due to multiple tests,
we performed 1000 permutations analyses.
In conclusion, our results showed that Thr394Thr SNP
in the PPARGC1 gene were associated with peak BMD in
Chinese women, but this SNP explains a very small
proportion of the variation in femoral neck BMD. Confirmation of
our results is needed in other populations and with more
functional markers within and flanking the PPARGC1 or
adiponectin gene region.
References
1 Rubin LA, Hawker GA, Peltekova VD, Fielding LJ, Ridout R,
Cole DE. Determinants of peak bone mass: clinical and genetic
analyses in a young female Canadian cohort. J Bone Miner Res
1999; 14: 633_43.
2 Brown LB, Streeten EA, Shapiro JR, McBride D, Shuldiner AR,
Peyser PA, et al. Genetic and environmental influences on bone
mineral density in pre- and post-menopausal women. Osteoporos
Int 2005; 16: 1849_56.
3 Peacock M, Koller DL, Hui S, Johnston CC, Foroud T, Econs
MJ. Peak bone mineral density at the hip is linked to
chromosomes 14q and 15q. Osteoporos Int 2004; 15: 489_96.
4 Glauber HS, Vollmer WM, Nevitt MC, Ensrud KE, Orwoll ES.
Bone weight versus body fat distribution, adiposity, and frame
size as predictors of bone density. J Clin Endocrinol Metab
1995; 80: 1118_23.
5 Zhang ZL, Qin YJ, Huang QR, Hu YQ, Li M, He JW,
et al. Bone mineral density of the spine and femur in healthy Chinese men.
Asia J Androl 2006; 8: 419_27.
6 Schott AM, Cormier C, Hans D, Favier F, Hausherr E,
Dargenet-Molina P, et al. How hip and whole-body bone mineral density
predict hip fracture in elderly women: The EPIDOS Prospective
Study. Osteoporos Int 1998; 8: 247_54.
7 Thomas T, Burguera B, Melton LJ III, Atkinson EJ, O'Fallon
WM, Riggs BL, et al. Relationship of serum leptin levels with
body composition and sex steroid and insulin levels in men and
women. Metabolism 2000; 49: 1278_84.
8 Staiger H, Haring HU. Adipocytokines: fat-derived humoral
mediators of metabolic homeostasis. Exp Clin Endocrinol
Diabetes 2005; 113: 67_79.
9 Lenchik L, Register TC, Hsu FC, Lohman K, Nicklas BJ,
Freedman BI, et al. Adiponectin as a novel determinant of bone
mineral density and visceral fat. Bone 2003; 33: 646_51.
10 Menzaghi C, Ercolino T, Di Pada R, Berg AH, Warram JH, Scherer
PE, et al. A haplotype at the adiponectin locus is associated with
obesity and other features of the insulin resistance syndrome.
Diabetes 2002; 51: 2306_12.
11 Esposito K, Pontillo A, Di Palo C, Giugliano G, Masella M,
Marfella R, et al. Effect of weight loss and lifestyle changes on
vascular inflammatory markers in obese women: a randomized
trial. JAMA 2003; 289: 1799_804.
12 Kontogianni MD, Dafni UG, Routsias JG, Skopouli FN. Blood
leptin and adiponectin as possible mediators of the relation
between fat mass and BMD in perimenopausal women. J Bone
Miner Res 2004; 19: 546_51.
13 Jurimae J, Rembel K, Jurimae T, Rehand M. Adiponectin is
associated with bone mineral density in perimenopausal women.
Horm Metab Res 2005; 37: 297_302.
14 Esterbauer H, Oberkofler H, Krempler F, Patsch W. Human
peroxisome proliferator activated receptor gamma coactivator
(PPARGC1) gene: cDNA sequence, genomic organization,
chromosomal localization, and tissue expression. Genomics 1999;
62: 98_102.
15 Perusse L, Rice T, Chagnon YC, Despres JP, Lemieux S, Roy S,
et al. A genomewide scan for abdominal fat assessed by computed
tomograghy in the Quebec Family Study. Diabetes 2001;
50:614_21.
16 Esterbauer H, Oberkofler H, Linnemayr V, Iglseder B, Hedegger
M, Wolfsgruber P, et al. Peroxisome proliferator-activated
receptor-γ coactivator-1 gene locus associations with obesity
indices in middle-ages women. Diabetes 2002; 51: 1281_6.
17 Liu XH, Liu YJ, Jiang DK, Li YM, Li MX, Qin YJ,
et al. No evidence for linkage and/or association of human alpha2-HS
glycoprotein gene with bone mineral density variation in Chinese
nuclear families. Calcif Tissue Int 2003; 73: 244_50.
18 Qin YJ, Zhang ZL, Huang QR, He JM, Hu YQ, Zhao Q,
et al. Association of vitamin D receptor and estrogen receptor-alpha
gene polymorphism with peak bone mass and bone size in
Chinese women. Acta Pharmacol Sin 2004; 25: 462_8.
19 Zhang ZL, Qin YJ, He JW, Huang QR, Li M, Hu YQ,
et al. Association of polymorphisms in low-density lipoprotein
receptor-related protein 5 gene with bone mineral density in
postmenopausal Chinese women. Acta Pharmacol Sin 2005; 26:
1111_6.
20 Hara K, Tobe K, Okada T, Kadowaki H, Akanuma Y, Ito C,
et al. A genetic variation in the PGC-1 gene could confer insulin
resistance and susceptibility to type II diabetes. Diabetologia 2002;
45: 740_3.
21 Ek J, Andersen G, Urhammer SA, Gaede PH, Drivsholm T,
Borch-Johnsen K, et al. Mutation analysis of peroxisome
proliferator-activated receptor-gamma coactivator-1 (PGC-1) and
relationships of identified amino acid polymorphisms to type II diabetes
mellitus. Diabetologia 2001; 44: 2220_6.
22 Sobel E, Lange K. Descent graphs in pedigree analysis:
Applications to haplotyping, location scores, and markersharing statistics.
Am J Hum Genet 1996; 58: 1323_37.
23 Abecasis GR, Cookson WO. Gold-graphical overview of linkage
disequilibrium. Bioinformatics 2000; 16: 182_3.
24 Abecasis GR, Cardon LR, Cookson WO. A general test of
association for quantitative traits in nuclear families. Am J Hum
Genet 2000; 66: 279_92.
25 Koller DL, Ichikawa S, Johnson ML, Lai D, Xuei X, Edenberg HJ,
et al. Contribution of the LRP5 gene to normal variation in
peak BMD in women. J Bone Miner Res 2005; 20: 75_80.
26 Qin YJ, Shen H, Huang QR, Zhao LJ, Zhou Q, Li MX,
et al. Estrogen receptor α gene polymorphisms and peak bone density
in Chinese nuclear families. J Bone Miner Res 2003; 18:
1028_35.
27 Deng FY, Liu MY, Li MX, Lei SF, Qin YJ, Zhou Q,
et al. Test of linkage and association of the COL1A2 gene with bone
phenotypes' variation in Chinese nuclear families. Bone 2003; 33:
614_19.
28 Ichikawa S, Koller DL, Johnson ML, Lai D, Xuei X, Edenberg HJ,
et al. Human ALOX12, but not ALOX15, is associated with
BMD in white men and women. J Bone Miner Res 2006; 21:
556_64.
29 Thijssen JH. Gene polymorphisms involved in the regulation of
bone quality. Gynecol Endocrinol 2006; 22: 131_9.
30 Esterbauer H, Oberkofler H, Linnemayr V, Iglseder B, Hedegger
M, Wolfsgruber P, et al. Peroxisome proliferator-activated
receptor-gamma coactivator-1 gene locus: associations with
obesity indices in middle-aged women. Diabetes 2002; 51: 1281_6.
31 Klein RF, Allard J, Avnur Z, Nikolcheva T, Rotstein D, Carlos
AS, et al. Regulation of bone mass in mice by the lipoxygenase
gene Alox15. Science 2004; 303: 229_32.
32 Huang JT, Welch JS, Ricote M, Binder CJ, Willson TM, Kelly C,
et al. Interleukin-4-dependent production of PPAR-gamma ligands
in macrophages by 12/15-lipoxygenase. Nature 1999; 400:
378_82.
33 Nosjean O, Boutin JA. Natural ligands of PPARgamma: are
prostaglandin J(2) derivatives really playing the part? Cell Signal
2002; 14: 573_83.
34 Pei L, Tontonoz P. Fat's loss is bone's gain. J Clin Invest 2004;
113: 805_6.
35 Akune T, Ohba S, Kamekura S, Yamaguchi M, Chung UI, Kubota
N, et al. PPARgamma insufficiency enhances osteogenesis
through osteoblast formation from bone marrow progenitors. J
Clin Invest 2004; 113: 846_55.
36 Ogawa S, Urano T, Hosoi T, Miyao M, Hoshino S, Fujita M,
et al. Association of bone mineral density with a polymorphism of the
peroxisom proliferator-activated receptor gamma gene:
PPARγ expression in osteoblasts. Biochem Biophys Res Commun 1999;
260: 122_6.
37 Takahashi M, Arita Y, Yamagata K, Matsukawa Y, Okutomi K,
Horie M, et al. Genomic structure and mutations in
adipose-specific gene, adiponectin. Int J Obes Relat Metab Disord 2000;
24: 861_8.
38 Yatagai T, Nagasaka S, Taniguchi A, Fukushima M, Nakamura T,
Kuroe A, et al. Hypoadiponectinemia is associated with visceral
fat accumulation and insulin resistance in Japanese men with
type 2 diabetes mellitus. Metabolism 2003; 52: 1274_8.
39 Oshima K, Nampei A, Matsuda M, Iwaki M, Fukuhara A,
Hashimoto J, et al. Adiponectin increases bone mass by
suppressing osteoclast and activating osteoblast. Biochem Biophys
Res Commun 2005; 331: 520_6.
40 Luo XH, Guo LJ, Yuan LQ, Xie H, Zhou HD, Wu XP,
et al. Adiponectin stimulates human osteoblasts proliferation and
differentiation via the MAPK signaling pathway. Exp Cell Res
2005; 309: 99_109.
|
