IntroductionOsteoporosis and tooth loss have been linked with advancing age, but no clear relationship between these conditions has been proven. Several studies of bone mineral density measurements of the jaw and spine have shown similarities in their rate of age-related deterioration. Thus, measurements of jawbone density may predict lumbar vertebral bone density. Using jawbone density as a proxy marker would circumvent the need for lumbar bone measurements and facilitate prediction of osteoporotic spinal fracture susceptibility at dental clinics. We aimed to characterize the correlation between bone density in the jaw and spine and the incidence of osteoporotic spinal fractures.MethodsWe used computerized radiogrammetry to measure alveolar bone mineral density (al-BMD) and dual-energy X-ray absorptiometry to measure lumbar bone mineral density (L-BMD). L-BMD and al-BMD in 30 female patients (average age: 59 ± 5 years) were correlated with various patient attributes. Statistical analysis included area under the curve (AUC) and probability of asymptomatic significance (PAS) in a receiver operating characteristic curve. The predictive strength of L-BMD T-scores (L-BMD[T]) and al-BMD measurements for fracture occurrence was then compared using multivariate analysis with category weight scoring.ResultsL-BMD and al-BMD were significantly correlated with age, years since menopause, and alveolar bone thickness. Both were also negatively correlated with fracture incidence. Category weight scores were −0.275 for a L-BMD(T) <80%; +0.183 for a L-BMD(T) ≥80%; −0.860 for al-BMD <84.9 (brightness); and +0.860 for al-BMD ≥84.9. AUC and PAS analyses suggested that al-BMD had a higher association with fracture occurrence than L-BMD.ConclusionsOur results suggest the possible association between al-BMD and vertebral fracture risk. Assessment of alveolar bone density may be useful in patients receiving routine dental exams to monitor the clinical picture and the potential course of osteoporosis in patients who may be at a higher risk of developing osteoporosis.

Accurate medical diagnosis is very important for patient treatment. The diagnostic framework is to connect the patient's information, X, such as clinical findings of pain, with the outcome of diagnosis, Y, such as cancer. The outcome Y sometimes includes both a decision regarding the disease and the classification of its grade. Moreover, some clinical indicators of X play important roles in the decision about disease and other indicators in the classification of disease. There is some uncertainty regarding information X and the outcome Y in the diagnosis and this is an aspect of "fuzziness" in medical diagnosis.As a new trial to clarify the fuzziness of diagnosis, we analyzed the clinical data on patients with Carpal Tunnel Syndrome (CTS) by the method of multivariate analysis.From the analytical results, we propose a new diagnostic system with two layers of diagnostic filters: A first Layer (for the decision about CTS) by the clinical indicators of Phalen's test and Ring sensory splitting and a second Layer (for the classification of CTS) by the indicators of Thenar muscle atrophy and Pinch disturbance.