Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study
J Clin Endocrinol Metab, May
Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study
Li Liu 0
Yan Wen 0
Lei Zhang 1
Peng Xu 2
Xiao Liang 0
Yanan Du 0
Ping Li 0
Awen He 0
QianRui Fan 0
Jingcan Hao 3
Wenyu Wang 0
Xiong Guo 0
Hui Shen 4
Qing Tian 4
Feng Zhang 0
Hong-Wen Deng 4
0 Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission of the People's Republic of China, School of Public Health, Health Science Center, Xi'an Jiaotong University , Xi'an 710061 , People's Republic of China
1 Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, School of Public Health, Medical College of Soochow University , Suzhou 215000 , People's Republic of China
2 Department of Joint Surgery, Xi'an Red Cross Hospital , Xi'an 710061 , People's Republic of China
3 The First Affiliated Hospital, Xi'an Jiaotong University , Xi'an 710061 , People's Republic of China
4 Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans 70112, Louisiana , USA
Context: Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive. Objective: To explore the relationship between blood metabolites and osteoporosis. Design and Methods: We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate. Results: We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (Pdiscovery , 0.0001; Preplication = 0.0361), inosine (Pdiscovery = 0.0018; Preplication = 0.0256), theophylline (Pdiscovery = 0.0048; Preplication = 0.0433, gamma-glutamyl methionine (Pdiscovery = 0.0047; Preplication = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; Pdiscovery = 0.0018; Preplication = 0.0390), and X-12127 (Pdiscovery = 0.0002; Preplication = 0.0249). Conclusions: Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis. (J Clin Endocrinol Metab 103: 1850-1855, 2018)
Oized as low bone mass and increased bone fragility.
steoporosis is a metabolic bone disease,
characterIt has been reported that currently .40 million people are
at risk of bone fractures, and the incidence of
osteoporotic fractures is increasing rapidly in the United States
). Bone mineral density (BMD) is commonly used to
*These authors contributed equally to this manuscript.
Abbreviations: 1,5-AG, 1,5-anhydroglucitol; BMD, bone mineral density; FHS,
Framingham Heart Study; GRS, genetic risk score; QPCT, pituitary glutaminyl cyclase
cyclotransferase; SNP, single nucleotide polymorphism.
diagnose osteoporosis and assess individual bone fracture
). BMD has a strong heritable component, with
an estimated heritability of 50% to 85% (
recent years, genome-wide association studies identified
.100 genetic loci associated with BMD (
instance, Estrada et al. (11) identified 56 BMD-associated
loci through a large scale genome-wide meta-analysis. By
integrating the genome-wide association studies and
transcriptomic gene expression data sets, Chen et al. (
detected several novel candidate genes for osteoporosis.
Although a group of susceptibility loci has been identified
for BMD, the genetic risks explained by the identified loci
are limited, suggesting the existence of undiscovered
susceptibility loci for osteoporosis.
Human blood metabolites, defined as the
intermediates and products of metabolism, vary widely across
different individuals. Noncellular metabolites (from plasma
or serum) are commonly used to explore their biological
significance and function owing to their convenience of
sample collection. Previous studies have attempted to
investigate the potential effects of blood metabolites on the
development of human complex diseases, such as diabetes
and cancer (
). Recently, Long et al. (15) conducted a
largescale whole-genome sequencing study to assess the
relationships between genetic variations and blood
metabolites using comprehensive metabolite profiling of
1960 adults. They identified a group of single nucleotide
polymorphisms (SNPs) associated with blood
metabolite levels (
Interest has increased in the relationships between
metabolites and osteoporosis. A recent study observed
novel metabolic profiling in postmenopausal women
with a low BMD (
). Another prospective study
suggested that intact parathyroid hormone and alkaline
phosphatase levels were the most important independent
factors associated with low BMD of the hip (
). To the
best of our knowledge, no largescale study has been
conducted to evaluate the relationship between global
blood metabolites and osteoporosis.
Mendelian randomization is an epidemiological
method in which environmental exposure-related genetic
variations as instrumental variables are used to evaluate
the association between environmental exposure and
disease outcomes (
). Mendelian randomization results
in a powerful control for confounding factors (e.g.,
lifestyle and socioeconomic factors) compared with
conventional epidemiologic studies (
). Recently, a
Mendelian randomization analysis was widely used to
investigate the effect of physiological variables on
diseases risks (
In the present study, we conducted a Mendelian
randomization analysis of 481 blood metabolites to
investigate the potential effect of blood metabolites on the
variations of BMD and bone areas. Our results might
help to reveal the association of blood metabolites with
osteoporosis and identify candidate blood metabolites for
association with osteoporosis.
Materials and Methods
A total of 2286 unrelated homogeneous white subjects living
in Kansas City and surrounding areas were enrolled in the
present study. Subjects with chronic diseases, conditions
involving vital organs (e.g., heart, lung, liver, kidney, and brain),
and severe endocrine, metabolic, or nutritional diseases were
excluded from the present study. In addition, subjects taking
antibone resorptive or bone anabolic agents or drugs, such as
bisphosphonates, were excluded from our study. BMD
(including total body, ulna and radius, hip, and spine) and bone
area (including ulna and radius, hip, and spine) were measured
using the Hologic 4500W dual energy X-ray absorptiometry
system (Hologic Inc., Bedford, MA) that were calibrated daily.
All phenotypic values were adjusted for age, sex, height, and
weight using a linear regression model. The institutional review
board of the University of Missouri Kansas City approved the
present study. All the participants signed informed consent
Genome-wide SNP genotyping
Genomic DNA was extracted from peripheral blood
leukocytes using the Puregene DNA isolation kit (Gentra Systems,
Minneapolis, MN). SNP genotyping was performed using the
Genome-Wide Human SNP Array, version 6.0 (Affymetrix,
Santa Clara, CA) following the Affymetrix protocol. In brief,
250-ng genomic DNA was digested with restriction enzyme
NspI or StyI. Digested DNA was adaptor-ligated and
polymerase chain reaction-amplified for each sample. Fragment
polymerase chain reaction products were then labeled with
biotin, denatured, and hybridized to the arrays. The arrays were
scanned using the GeneChip Scanner 3000 7G (Affymetrix).
Data management and analysis was conducted using the
GeneChip? Command Console? software (Affymetrix). After
quality control, we excluded 3930 SNPs with Hardy-Weinberg
equilibrium testing P values of , 0.0001 and 145,204 SNPs
with minor allele frequencies P values of , 0.01. In addition, 36
study subjects with a SNP call rate of , 0.95 were excluded.
Framingham Heart Study replication samples
The Framingham Heart Study (FHS) cohort was accessed
through the database of Genotypes and Phenotypes (available at:
https://www.ncbi.nlm.nih.gov/gap; access no. phs000342.v14.p10).
In brief, FHS is a longitudinal and prospective cohort
comprising .16,000 pedigree participants. The BMD values of the
hip and spine were measured using dual-energy X-ray
absorptiometry (Lunar Corp., Madison, WI). The
participants were genotyped using the high-throughput Affymetrix
500K genotyping array plus a supplemental Affymetrix 50K
genotyping array. Two genotype sets were merged together to
form a single data set of ;550,000 SNPs to maximize genotype
coverage. Detailed information of the study design and sample
recruitment was described in previous studies (
for the present study, a total of 3143 unrelated white subjects
from the FHS were used as replication samples.
Blood metabolite-associated SNP sets
Blood metabolite-associated SNPs and their genetic effects
were driven from a largescale whole-genome sequencing study
of blood metabolites (
). In brief, Long et al. (
whole-genome sequencing of 1960 adults to assess the
association between genetic variations and blood metabolite levels.
The serum samples were collected at three clinical visits
for a period of 18 years and were analyzed on a nontarget
metabolomics platform. Metabolite profiling was performed
using the Metabolon platform (Metabolon, Morrisville, NC).
Association tests were conducted using a linear mixed mode.
Detailed descriptions of the sample collection, experimental
design, quality control, and statistical analysis are available in
the previously reported study (
). For the present study, the
SNP sets significantly associated with blood metabolites at
the genome-wide significance level were selected for
individual genetic risk score (GRS) calculation (
calculated GRS was then used as the instrumental variable of the
corresponding blood metabolite to evaluate the possible
association between the blood metabolite and target trait.
We analyzed 481 blood metabolites in the present study.
Following the standard approach used by recent Mendelian
randomization studies (
), the GRS of each metabolite was
calculated from SNP genotype data for each study subject.
GRSjm denoted the GRS value of the jth metabolite for the mth
subject, defined as GRSjm ? li?1biSNPim. bi indicates the effect
parameter of the risk allele of the ith significant SNP for the jth
metabolite, which was obtained from the previously reported
). SNPim was the dosage (
0, 1, 2
) of the risk allele of the
ith SNP for the mth study subject. Using the calculated GRS as
the instrumental variable of the blood metabolites, a Pearson
correlation analysis of the individual GRS values and BMD
values (adjusted for age, sex, height, and weight as covariates)
was conducted to evaluate the possible association between
each blood metabolite and target trait. Through randomly
shuffling of the phenotypic values of study subjects, 10,000
permutations were conducted to obtain the empirical
distributions of testing statistics of the Pearson correlation analysis
for each pair of blood metabolites and target traits. Empirical
P values and the false discovery rate were then calculated from
the obtained empirical distributions. The important blood
metabolites detected in the discovery samples were further
validated in the FHS replication samples. All statistical analyses
were performed using R (available at: https://www.r-project.org/).
We observed important associations between the total hip
BMD and 54 blood metabolites (Supplemental Table 1),
including 8 blood metabolites with a false discovery rate of ,0.01
(Fig. 1). The eight blood metabolites were 1,5-anhydroglucitol
(1,5-AG; Pdiscovery , 0.0001), guanidinoacetate (Pdiscovery =
0.0002), pyroglutamine (Pdiscovery = 0.0002), deoxycarnitine
(Pdiscovery , 0.0001), 1-methylimidazoleacetate (Pdiscovery =
0.0002), gamma-glutamylhistidine (Pdiscovery , 0.0001),
phenylacetate (Pdiscovery , 0.0001), and X-10358 (Pdiscovery ,
0.0001). The 54 blood metabolites identified in the discovery
samples were further replicated in the FHS samples. For
hip BMD, we also detected association signals for 1,5-AG
(Preplication = 0.0361), inosine (Preplication = 0.0256), theophylline
(Preplication = 0.0433) gamma-glutamylmethionine (Preplication =
0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6)
(Preplication = 0.0390), and X-12127 (Preplication = 0.0249).
For spinal BMD, association signals were observed for
Nacetyltaurine (Preplication = 0.0119), gamma-glutamylmethionine
(Preplication = 0.0124), and inosine (Preplication = 0.0178). The
absolute values of the Pearson correlation coefficients of all
substantial associations were ,0.1, indicating the weak effects of
blood metabolites on the variations of BMD.
To reveal the potential effects of blood metabolites on the
development of osteoporosis, we conducted a Mendelian
randomization analysis of 481 blood metabolites using
2286 white subjects. We observed modest associations of
blood metabolites with the variations of BMD and
identified several candidate blood metabolites for an
association with osteoporosis. To the best of our knowledge,
the present study is the first largescale Mendelian
randomization study of blood metabolites for osteoporosis.
Our study results could help to reveal the effect of blood
metabolites on the development of BMD and provide clues
for pathogenetic studies of osteoporosis.
We found that guanidinoacetate, 1,5-AG, and
pyroglutamine were substantially associated with total
hip BMD. Guanidinoacetate (or glycocyamine) is a
metabolite of glycine, as well as the direct precursor of
creatine. The biological role of creatine is to facilitate the
recycling of adenosine triphosphate, mainly in brain and
muscle. Emerging evidence has suggested that creatine
supplementation could affect bone biology. For instance,
an animal study conducted in young Sprague-Dawley rats
suggested that creatine supplementation had beneficial
effects on the biological function and structure of bone
and increased lumbar BMD (
). A recent study indicated
that the combination of 12 months of creatine
supplementation and resistance training preserved femoral neck
BMD and increased femoral shaft superiosteal width in
postmenopausal women (
). The same conclusion was
also drawn in the elderly (
), supporting the implication
of creatine in maintaining the normal biological function
1,5-AG, a naturally occurring polyol, serves as a
valuable complement to frequent self-monitoring or
continuous monitoring of plasma glucose to confirm
stable glycemic control (
). 1,5-AG can be found in
various foods. A previous study of patients with
moderately controlled diabetes demonstrated that 1,5-AG
was associated with the risk of diabetes in the
postprandial state and was more sensitive and specific than
fructosamine and hemoglobin A1c (
). In addition, the
association between diabetes and bone metabolism has
achieved wide attention. Researchers observed that older
adults with type 2 diabetes tended to have normal or
greater BMD than those without diabetes (
instance, Oei et al. (
) found that poor glycemic control in
those with type 2 diabetes was associated with a high
BMD in narrower bones. It is interesting that the subjects
with type 2 diabetes were reported to be associated with
an increased fracture risk despite an increased BMD (
Theophylline is another common metabolite detected
in both discovery samples and replication samples. It is a
methylxanthine drug with anti-inflammatory property.
Studies of theophylline on bone cells, skeleton, and
system calcium homeostasis have resulted in widespread
attention. Studies have shown that the parameters related
to systemic calcium homeostasis of theophylline-treated
animals changed significantly compared with control
animals, including increased urinary calcium excretion
and decreased total body calcium (
). These results have
shown the promotional effects of theophylline on skeletal
calcium loss and its acceleration of the development of
human osteopenia (
). The same conclusion regarding
the detrimental effect of theophylline on bone loss was
reported in another study (
Pyroglutamine is a notable blood metabolite
associated with total hip BMD. Pyroglutamine is a cyclic
derivative of glutamine related to pyroglutamic acid. The
pituitary glutaminyl cyclotransferase (QPCT) gene
encodes an enzyme responsible for the presence of
pyroglutamyl residues in neuroendocrine peptides. A study
conducted in Japanese subjects found that common
polymorphisms in the QPCT gene were significantly
associated with BMD in postmenopausal women (
Huang and Kung (
) also found that rs3770748 in QPCT
was associated with spinal BMD in Chinese subjects.
Apart from the top eight metabolites, some other
important metabolites were also associated with BMD
in our study. For example, gamma-glutamylmethionine,
one of the gamma-glutamyl derivatives, was used to
examine the transport of gamma-glutamyl amino acids
into tissues in the mouse. A previous study conducted
in Japanese subjects found that a functional SNP in
the vitamin K-dependent gamma-glutamyl carboxylase
gene (Arg325Gln) was associated with BMD in elderly
Japanese women (
). Gamma-glutamyltransferase is a
type of enzyme that plays a key role in the
gammaglutamyl cycle. The serum gamma-glutamyltransferase
level within its normal range is inversely correlated with
the BMD in the femur neck among postmenopausal
One limitation of the present study was that the
Pearson correlation analysis coefficients of all substantial
blood metabolites were ,0.1, indicating the limited
strength of the association of blood metabolites on the
variation of BMD. These results were consistent with
previous study results, which demonstrated that
osteoporosis is a complex diseases determined by a group of
genetic and environmental factors (
). Each factor
had limited effects on the risks of osteoporosis (
addition, the relatively small sample size might have
limited the statistical power of our study. Our study
results should be interpreted with caution. Further studies
are needed to confirm our findings and clarify the
potential molecular mechanisms of the detected associations
between blood metabolites and BMD.
In conclusion, using Mendelian randomization
analysis, we investigated the effect of global blood metabolites
on the variations of BMD. Our study results suggest the
modest effects of the associations of blood metabolites on
the variation in BMD and identified several candidate
blood metabolites for association with osteoporosis. We
hope that our study results have provided clues for the
pathogenetic studies of osteoporosis.
Financial Support: The present study was partially
supported by the National Natural Scientific Foundation of China
(grants 81472925 and 81673112), Technology Research and
Development Program of Shaanxi Province of China (grant
2013KJXX-51), and Fundamental Research Funds for the
Central Universities. Q.T., H.S., and H.W.D. were partially
supported by grants from the National Institutes of Health
(grants R01AR057049, R01AR059781, D43TW009107,
P20 GM109036, R01MH107354, R01MH104680, and
R01GM109068), Edward G. Schlieder Endowment fund, and
Tsai and Kung endowment fund to Tulane University. The
Framingham Heart Study is conducted and supported by
the National Heart, Lung, and Blood Institute (NHLBI) in
collaboration with Boston University (contract no.
N01-HC25195). The present report was not prepared in collaboration
with investigators of the Framingham Heart Study and does not
necessarily reflect the opinions or views of the Framingham
Heart Study, Boston University, or NHLBI. Funding for SHARe
Affymetrix genotyping was provided by the NHLBI (contract
N02-HL-64278). SHARe Illumina genotyping was provided
under an agreement between Illumina and Boston University.
Funding support for the Framingham Whole Body and Regional
Dual X-ray Absorptiometry data set was provided by National
Institutes of Health (grant R01 AR/AG 41398). The data sets
used for the analyses described in our report were obtained from
database of Genotypes and Phenotypes (available at: http://www.
ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession
Correspondence and Reprint Requests: Feng Zhang,
School of Public Health, Xi?an Jiaotong University Health
Science Center, No.76 Yan Ta West Road, Xi?an 710061,
People?s Republic of China. E-mail: ;
or Hong-Wen Deng, School of Public Health and Tropical
Medicine, Tulane University, 1440 Canal Street, Room 1619F,
New Orleans, LA 70112. E-mail: .
Disclosure Summary: The authors have nothing to
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