Date Published: May 29, 2019
Publisher: Public Library of Science
Author(s): T. A. Pontes, A. D. Barbosa, R. D. Silva, M. R. Melo-Junior, R. O. Silva, Daniel Monleon.
This is a report on how 1H NMR-based metabonomics was employed to discriminate osteopenia from osteoporosis in postmenopausal women, identifying the main metabolites associated to the separation between the groups. The Assays were performed using seventy-eight samples, being twenty-eight healthy volunteers, twenty-six osteopenia patients and twenty-four osteoporosis patients. PCA, LDA, PLS-DA and OPLS-DA formalisms were used. PCA discriminated the samples from healthy volunteers from diseased patient samples. Osteopenia-osteoporosis discrimination was only obtained using Analysis Discriminants formalisms, as LDA, PLS-DA and OPLS-DA. The metabonomics model using LDA formalism presented 88.0% accuracy, 88.5% specificity and 88.0% sensitivity. Cross-Validation, however, presented some problems as the accuracy of modeling decreased. LOOCV resulted in 78.0% accuracy. The OPLS-DA based model was better: R2Y and Q2 values equal to 0.871 (p<0.001) and 0.415 (p<0.001). LDA and OPLS-DA indicated the important spectral regions for discrimination, making possible to assign the metabolites involved in the skeletal system homeostasis, as follows: VLDL, LDL, leucine, isoleucine, allantoin, taurine and unsaturated lipids. These results indicate that 1H NMR-based metabonomics can be used as a diagnosis tool to discriminate osteoporosis from osteopenia using a single serum sample.
Osteoporosis is a multifactorial systemic skeletal disease that causes damage to the microarchitecture of bone tissue, increasing the risk of fractures . Women in the postmenopausal period are the most affected by this problem because of the hormonal deficiency that occurs during this period. Reduction of estrogen levels promotes the homeostatic imbalance of the bone remodeling process, causing an increase in bone resorption, deterioration of the microarachitecture, and a decrease in bone mass. About 40% of women older than 50 years of age are diagnosed with postmenopausal osteoporosis, making it necessary to pay special attention to this patient group . Estrogen hormone therapy has been considered the most effective for the prevention and treatment of postmenopausal osteoporosis. However, investigations showed that estrogen could lead to higher occurrences of endometrial cancer, stroke, cardiovascular diseases and breast carcinoma 
Table 1 shows clinical data of participants of each group, while Fig 1 presents a typical 1H NMR spectrum of serum obtained in this study and main assignments, identifying associated metabolites.
International Osteoporosis Foundation  data indicate that osteoporosis was diagnosed in more de 200 million women and is associated with 9 million fractures annually in the world. Generally, osteoporosis is associated to women and to aging, but can be diagnosed in the young and also in men. About 33% of women over 45 years old have a positive diagnosis for osteoporosis. When women over 80 are observed, the disease reaches about 73% of this population. This indicates that is important to develop diagnostic tools that are able to discriminate osteoporosis from osteopenia patients in this group (postmenopausal women). The present study investigated only postmenopausal women, aiming to discriminate osteopenia from osteoporosis, using 1H NMR-based metabonomics. There were three groups–Healthy (28 volunteers), osteopenia (26 patients) and osteoporosis (24 patients).