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Metabolomics-based profiles predictive of low bone mass in menopausal women.

ABSTRACT: Osteoporosis is a skeletal disorder characterized by compromised bone strength and increased risk of fracture. Low bone mass and/or pre-existing bone fragility fractures serve as diagnostic criteria in deciding when to start medication for osteoporosis. Although osteoporosis is a metabolic disorder, metabolic markers to predict reduced bone mass are unknown. Here, we show serum metabolomics profiles of women grouped as pre-menopausal with normal bone mineral density (BMD) (normal estrogen and normal BMD; NN), post-menopausal with normal BMD (low estrogen and normal BMD; LN) or post-menopausal with low BMD (low estrogen and low BMD; LL) using comprehensive metabolomics analysis. To do so, we enrolled healthy volunteer and osteoporosis patient female subjects, surveyed them with a questionnaire, measured their BMD, and then undertook a comprehensive metabolomics analysis of sera of the three groups named above. We identified 24 metabolites whose levels differed significantly between NN/LN and NN/LL groups, as well as 18 or 10 metabolites whose levels differed significantly between NN/LN and LN/LL, or LN/LL and NN/LN groups, respectively. Our data shows metabolomics changes represent useful markers to predict estrogen deficiency and/or bone loss.

SUBMITTER: Miyamoto T

PROVIDER: S-EPMC6019687 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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Metabolomics-based profiles predictive of low bone mass in menopausal women.

Miyamoto Takeshi T Hirayama Akiyoshi A Sato Yuiko Y Koboyashi Tami T Katsuyama Eri E Kanagawa Hiroya H Fujie Atsuhiro A Morita Mayu M Watanabe Ryuichi R Tando Toshimi T Miyamoto Kana K Tsuji Takashi T Funayama Atsushi A Soga Tomoyoshi T Tomita Masaru M Nakamura Masaya M Matsumoto Morio M

Bone reports 20180618

Osteoporosis is a skeletal disorder characterized by compromised bone strength and increased risk of fracture. Low bone mass and/or pre-existing bone fragility fractures serve as diagnostic criteria in deciding when to start medication for osteoporosis. Although osteoporosis is a metabolic disorder, metabolic markers to predict reduced bone mass are unknown. Here, we show serum metabolomics profiles of women grouped as pre-menopausal with normal bone mineral density (BMD) (normal estrogen and no ...[more]

PMID: 29955645

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Project description:BackgroundWomen experience drastic hormonal changes during midlife due to the menopausal transition. Menopausal hormonal changes are known to lead to bone loss and potentially also to loss of lean mass. The loss of muscle and bone tissue coincide due to the functional relationship and interaction between these tissues. If and how physical activity counteracts deterioration in muscle and bone during the menopausal transition remains partly unresolved. This study investigated differences between premenopausal, early perimenopausal, late perimenopausal, and postmenopausal women in appendicular lean mass (ALM), appendicular lean mass index (ALMI), femoral neck bone mineral density (BMD) and T score. Furthermore, we investigated the simultaneous associations of ALM and BMD with physical activity in the above-mentioned menopausal groups.MethodsData from the Estrogen Regulation of Muscle Apoptosis study were utilized. In total, 1393 women aged 47-55 years were assigned to premenopausal, early perimenopausal, late perimenopausal, and postmenopausal groups based on follicle-stimulating hormone concentration and bleeding diaries. Of them, 897 were scanned for ALM and femoral neck BMD by dual-energy X-ray absorptiometry and ALMI (ALM/height2 ) and neck T scores calculated. Current level of leisure-time physical activity was estimated by a validated self-report questionnaire and categorized as sedentary, low, medium, and high.ResultsAppendicular lean mass, appendicular lean mass index, femoral neck bone mineral density, and and T score showed a significant linear declining trend across all four menopausal groups. Compared with the postmenopausal women, the premenopausal women showed greater ALM (18.2, SD 2.2 vs. 17.8, SD 2.1, P < 0.001), ALMI (6.73, SD 0.64 vs. 6.52, SD 0.62, P < 0.001), neck BMD (0.969, SD 0.117 vs. 0.925, SD 0.108, P < 0.001), and T score (-0.093, SD 0.977 vs -0.459, SD 0.902, P < 0.001). After adjusting for potential confounding pathways, a higher level of physical activity was associated with greater ALM among the premenopausal [β = 0.171; confidence interval (CI) 95% 0.063-0.280], late perimenopausal (β = 0.289; CI 95% 0.174-0.403), and postmenopausal (β=0.278; CI 95% 0.179-0.376) women. The positive association between femoral neck BMD and level of physical activity was significant only among the late perimenopausal women (β = 0.227; CI 95% 0.097-0.356).ConclusionsSkeletal muscle and bone losses were associated with the menopausal transition. A higher level of physical activity during the different menopausal phases was beneficial, especially for skeletal muscle. Menopause-related hormonal changes predispose women to sarcopenia and osteoporosis and further to mobility disability and fall-related fractures in later life. New strategies are needed to promote physical activity among middle-aged women. Longitudinal studies are needed to confirm these results.

Project description:Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

Dataset Information

Metabolomics-based profiles predictive of low bone mass in menopausal women.

Publications

Metabolomics-based profiles predictive of low bone mass in menopausal women.

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