203 research outputs found

    Baseline characteristics for participants.

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    <p>HRT: Hormone replacement therapy</p><p>*999 female and 768 male individuals with available measure of BMD at the second round</p><p>**1,403 female and 1,118 male individuals with available measure of hip bone geometry</p><p>Baseline characteristics for participants.</p

    The association between metabolic syndrome, osteopenia and osteoporosis in women and men.

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    <p>Reference group are subjects with no osteopenia, neither osteoporosis: Confounders include age, body mass index, height, smoking status, physical activity, alcohol intake, fallings in the last 12 months, use of diuretics drugs, use of hormone replacement therapy, use of corticosteroids drugs, use of drugs for bone and other musculoskeletal diseases and Dutch Healthy Diet Index.</p

    The longitudinal association of metabolic syndrome with bone mineral density.

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    <p>MS, metabolic syndrome; FN-BMD, femoral neck bone mineral density</p><p>Model 1: Adjusted for age and type of DXA scan</p><p>Model 2: Model 1 +body mass index and height</p><p>Model 3: Model 2 + smoking status, physical activity, alcohol intake, fallings in the last 12 months, use of diuretics drugs, use of hormone replacement therapy, use of corticosteroids drugs, use of drugs for bone and other musculoskeletal diseases and Dutch Healthy Diet Index.</p><p>*index time (time points when the DXA measurements were performed), β = -0.012, p<0.001; interaction MS x index time: β = -0.008, p = 0.031</p><p>** index time, β = -0.012, p<0.001; interaction MS component x index time: β = -0.003, p = 0.021</p><p>#no significant interaction between MS (or MS component) and index time (p>0.50) was observed in any of the analysis in men and therefore data are not shown</p><p>The longitudinal association of metabolic syndrome with bone mineral density.</p

    Level of glycemic derangement, bone architecture and fracture risk.

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    <p>Cartoon depicting the differences in bone mineral density, fracture risk and changes in bone microarchitecture across the stages of glucose derangement. Metabolic syndrome and diabetes mellitus individuals have higher BMD but do not experience yet an increase in fracture risk. With sustained bad glycemic control, the damage of bone microarchitecture represented by accumulation of microcracks and cortical porosity becomes a possibility which may explain the bone fragility and fracture susceptibility despite the observed increase in BMD. Drawing is not to scale.</p

    Metabolic syndrome and fracture risk.

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    <p>“( )”, number of fractures</p><p>Model 1: Adjusted for age</p><p>Model 2: Model 1 +Height and Weight</p><p>Model 3: Model 2 + smoking status, physical activity, alcohol intake, fallings in the last 12 months, use of diuretics drugs, use of hormone replacement therapy, use of corticosteroids drugs, use of drugs for bone and other musculoskeletal diseases and Dutch Healthy Diet Index.</p><p>Metabolic syndrome and fracture risk.</p

    Identifying potential causal effects of age at menarche: a Mendelian randomization phenome-wide association study

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    Background: Age at menarche has been associated with various health outcomes. We aimed to identify potential causal effects of age at menarche on health-related traits in a hypothesis-free manner. Methods: We conducted a Mendelian randomization phenome-wide association study (MR-pheWAS) of age at menarche with 17,893 health-related traits in UK Biobank (n = 181,318) using PHESANT. The exposure of interest was the genetic risk score for age at menarche. We conducted a second MR-pheWAS after excluding SNPs associated with BMI from the genetic risk score, to examine whether results might be due to the genetic overlap between age at menarche and BMI. We followed up a subset of health-related traits to investigate MR assumptions and seek replication in independent study populations. Results: Of the 17,893 tests performed in our MR-pheWAS, we identified 619 associations with the genetic risk score for age at menarche at a 5% false discovery rate threshold, of which 295 were below a Bonferroni-corrected P value threshold. These included potential effects of younger age at menarche on lower lung function, higher heel bone-mineral density, greater burden of psychosocial/mental health problems, younger age at first birth, higher risk of childhood sexual abuse, poorer cardiometabolic health, and lower physical activity. After exclusion of variants associated with BMI, the genetic risk score for age at menarche was related to 37 traits at a 5% false discovery rate, of which 29 were below a Bonferroni-corrected P value threshold. We attempted to replicate findings for bone-mineral density, lung function, neuroticism, and childhood sexual abuse using 5 independent cohorts/consortia. While estimates for lung function, higher bone-mineral density, neuroticism, and childhood sexual abuse in replication cohorts were consistent with UK Biobank estimates, confidence intervals were wide and often included the null. Conclusions: The genetic risk score for age at menarche was related to a broad range of health-related traits. Follow-up analyses indicated imprecise evidence of an effect of younger age at menarche on greater bone-mineral density, lower lung function, higher neuroticism score, and greater risk of childhood sexual abuse in the smaller replication samples available; hence, these findings need further exploration when larger independent samples become available

    Generic Platform for the Multiplexed Targeted Electrochemical Detection of Osteoporosis-Associated Single Nucleotide Polymorphisms Using Recombinase Polymerase Solid-Phase Primer Elongation and Ferrocene-Modified Nucleoside Triphosphates

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    Osteoporosis is a multifactorial disease influenced by genetic and environmental factors, which contributes to an increased risk of bone fracture, but early diagnosis of this disease cannot be achieved using current techniques. We describe a generic platform for the targeted electrochemical genotyping of SNPs identified by genome-wide association studies to be associated with a genetic predisposition to osteoporosis. The platform exploits isothermal solid-phase primer elongation with ferrocene-labeled nucleoside triphosphates. Thiolated reverse primers designed for each SNP were immobilized on individual gold electrodes of an array. These primers are designed to hybridize to the SNP site at their 3′OH terminal, and primer elongation occurs only where there is 100% complementarity, facilitating the identification and heterozygosity of each SNP under interrogation. The platform was applied to real blood samples, which were thermally lysed and directly used without the need for DNA extraction or purification. The results were validated using Taqman SNP genotyping assays and Sanger sequencing. The assay is complete in just 15 min with a total cost of 0.3€ per electrode. The platform is completely generic and has immense potential for deployment at the point of need in an automated device for targeted SNP genotyping with the only required end-user intervention being sample addition

    GWS SNPs associated with Behçet’s disease susceptibility using GPC and EMMAX approaches.

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    <p>Results for the association analysis when correcting for 20 PCs to adjust for stratification. In italic font the SNPs which show association only by one of the two approaches.</p><p>* Odd ratios cannot be calculated by EMMAX approach.</p><p>GWS SNPs associated with Behçet’s disease susceptibility using GPC and EMMAX approaches.</p

    Behcet GWAS results using Linear Mixed Models Genomic approach.

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    <p>Each dot represents an SNP in the dataset. QQ-plot (left). Associated SNPs deviating from the null hypothesis of no association (identity line). Manhattan plot (right). SNPs showing association with the disease map to two different signals in chromosome 6 and a singleton in chromosome 18.</p

    Genetic substructure of the Combined GWAS dataset.

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    <p>Two-dimensional scatterplots from multidimensional scaling analyses of the Generation R Study and Behçet collected data together with the three initial Panels form the HapMap Project. Each dot represents an individual in the dataset. Color codes: Grey = Generation R, Black = Behcet Cases, Yellow = Jordan controls. Blue = CEU, Red = YRB, Green = JPT.</p
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