Changes in bone turnover with years since menopause (YSM) are responsible for bone loss and play a major role in osteoporosis. Although single measurements of the bone turnover marker appear unlikely to be clinically useful in predicting bone mineral density, the usefulness of these measurements in relation to the YSM has not been well established. The establishment of this relationship was the aim of this study. To address this issue, we have measured a battery of sensitive and specific markers of bone turnover in 272 women postmenopausal from -5 to 15 a, and the data was correlated with bone mineral density (BMD) at different skeletal sites measured utilizing dual-energy X-ray absorptiometry (DXA). Bone formation was assessed by serum osteocalcin (OC), and bone resorption by Pyr and D-pyr. The three markers and BMD were compared between the groups (YSM). Among the three markers, only Pyr exhibited a significant difference between pre and postmenopausal groups. In the aspect of correlation between bone turnover marker and BMD according to the groups (YSM), we found negative strong correlations between the BMD of lumbar spine (L2-4) vs. Pyr (P=0.01, r=-0.75) in the premenopausal group (-5 approximately 0 YSM), and we found negative correlation between the BMD of L2-4 vs. osteocalcin (P=0.05, r=-0.2 and P=0.01, r=-4).44) in the postmenopause groups (0 approximately 5 and 5 approximately 10 YSM). We concluded that Pyr in women -5 approximately 0 YSM and osteocalcin in women 0 approximately 10 YSM displayed negative correlation with BMD of L2-4.
Both pelvic organ prolapse (POP) and osteoporosis are age-related diseases in older aged women. Both POP and bone metabolism may be associated with collagen metabolism. Our study determined the relationship between POP and bone mineral density (BMD) of the lumbar spine and femur neck in postmenopausal women. We selected 554 postmenopausal women (aged 50-79 years) and divided them into two groups (moderate to severe POP and absent to mild POP). We compared the BMDs of the lumbar spine and femur neck between the moderate to severe POP and absent to mild POP groups. Lumbar spine BMD was inversely correlated with POP severity (p = 0.001). However, after adjusting for age, time since menopause, height, weight, body mass index (BMI), and vaginal delivery, the BMDs of both the lumbar spine and femur neck were not significantly different between the moderate to severe POP and absent to mild POP groups (p = 0.583 and p = 0.305, respectively). A lower BMD is associated with increased fracture risk and we postulated that women with severe POP would have an increased risk of osteoporotic fracture.
Heterogeneity of glucose abnormalities makes prevention and treatment of diabetes challenging. Subclasses of people at high risk have been identified based on glucose profiles during OGTTs. However, previous work on subclasses lacks longitudinal confirmation. Here, we used glucose and insulin during OGTTs and k-means clustering to classify subclasses of people without diabetes and then determined the model-derived beta-cell function (BCF) and insulin sensitivity (IS) within each subclass. We identified metabolic risk and the most common pathway to T2D by tracking transitions of subclasses over the 16-year Korean Genome Epi. Study. Participants underwent OGTTs every 2 years for up to 16 years or until diabetes (N=2682, BMI=24.4±2.9 kg/m2, age 49.8±7.6 y). 1) 6 subclasses: Cluster A: Normal BCF, Normal IS, B: Mildly impaired BCF and IS, C: Impaired BCF, mildly impaired IS, D: Strong BCF, impaired IS, E: Mildly impaired BCF, impaired IS, F: Strong BCF, severely impaired IS; 2) Cluster C had the highest risk for T2D, HR: 10.7 (8.0, 14.1), compared to cluster A, followed by Clusters E, B, F, and D; Transition from A to B and C was the most common pathway; A, B, and C together comprised 86% of the cohort. Clustering using glucose and insulin classified 6 subgroups showing distinct metabolic parameters and identified the most common pathway to T2D in Koreans. Disclosure J.Kim: None. H.Kang: None. S.T.Chung: None. A.Sherman: None. S.Kim: None. J.Ha: None. W.Yi: None. D.Kim: None. M.Im: None. S.Ryang: None. M.Kim: None. Y.Kim: None. I.Kim: None. Y.Kim: None. Funding Busan Economic Promotion Agency; National Research Foundation of Korea (2022H1D3A2A01063552); Korea Health Industry Development Institute (HI18C2383); dkNET New Investigator Pilot Program in Bioinformatics; National Institute of Diabetes and Digestive and Kidney Diseases
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