Links between cardiovascular disease and osteoporosis in postmenopausal women: serum lipids or atherosclerosis per se?

INTRODUCTION AND HYPOTHESIS: Epidemiological observations suggest links between osteoporosis and risk of acute cardiovascular events and vice versa. Whether the two clinical conditions are linked by common pathogenic factors or atherosclerosis per se remains incompletely understood. We investigated whether serum lipids and polymorphism in the ApoE gene modifying serum lipids could be a biological linkage. METHODS: This was an observational study including 1176 elderly women 60–85 years old. Women were genotyped for epsilon (ɛ) allelic variants of the ApoE gene, and data concerning serum lipids (total cholesterol, triglycerides, HDL-C, LDL-C, apoA1, ApoB, Lp(a)), hip and spine BMD, aorta calcification (AC), radiographic vertebral fracture and self-reported wrist and hip fractures, cardiovascular events together with a wide array of demographic and lifestyle characteristics were collected. RESULTS: Presence of the ApoE ɛ4 allele had a significant impact on serum lipid profile, yet no association with spine/hip BMD or AC could be established. In multiple regression models, apoA1 was a significant independent contributor to the variation in AC. However, none of the lipid components were independent contributors to the variation in spine or hip BMD. When comparing the women with or without vertebral fractures, serum triglycerides showed significant differences. This finding was however not applicable to hip or wrist fractures. After adjustment for age, severe AC score (≥6) and/or manifest cardiovascular disease increased the risk of hip but not vertebral or wrist fractures. CONCLUSION: The contribution of serum lipids to the modulators of BMD does not seem to be direct but rather indirect via promotion of atherosclerosis, which in turn can affect bone metabolism locally, especially when skeletal sites supplied by end-arteries are concerned. Further studies are needed to explore the genetic or environmental risk factors underlying the association of low triglyceride levels to vertebral fractures

Depot-Dependent Effects of Adipose Tissue Explants on Co-Cultured Hepatocytes

We have developed an in vitro hepatocyte-adipose tissue explant (ATE) co-culture model enabling examination of the effect of visceral and subcutaneous adipose tissues on primary rat hepatocytes. Initial analyses of inflammatory marker genes were performed in fractionated epididymal or inguinal adipose tissues. Expressions of inflammation related genes (IL-6, TNF-α, COX-2) were higher in the inguinal than the epididymal ATE. Similarly, expressions of marker genes of macrophage and monocyte (MPEG-1, CD68, F4/80, CD64) were higher in the stromal vascular fraction (SVF) isolated from inguinal ATE than that from epididymal ATE. However, expressions of lipolysis related genes (ATGL, HSL, perilipin-1) were higher in the epididymal adipocytes than inguinal adipocytes. Moreover, secretion of IL-6 and PGE2 was higher from inguinal ATEs than from epididymal ATEs. There was a trend that the total levels of IL-6, TNF-α and PGE2 in the media from inguinal ATEs co-cultured with primary rat hepatocytes were higher than that in the media from epididymal ATEs co-cultured with hepatocytes, although the significant difference was only seen in PGE2. Lipolysis, measured as glycerol release, was similar in the ATEs isolated from inguinal and epididymal adipose tissues when cultured alone, but the glycerol release was higher in the ATEs isolated from epididymal than from inguinal adipose tissue when co-cultured with hepatocytes. Compared to epididymal ATEs, the ATEs from inguinal adipose tissue elicited a stronger cytotoxic response and higher level of insulin resistance in the co-cultured hepatocytes. In conclusion, our results reveal depot-dependent effects of ATEs on co-cultured primary hepatocytes, which in part may be related to a more pronounced infiltration of stromal vascular cells (SVCs), particularly macrophages, in inguinal adipose tissue resulting in stronger responses in terms of hepatotoxicity and insulin-resistance

Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database

Objectives Osteoarthritis (OA) is a complex disease comprising diverse underlying patho-mechanisms. To enable the development of effective therapies, segmentation of the heterogenous patient population is critical. This study aimed at identifying such patient clusters using two different machine learning algorithms. Methods Using the progression and incident cohorts of the Osteoarthritis Initiative (OAI) dataset, deep embedded clustering (DEC) and multiple factor analysis with clustering (MFAC) approaches, including 157 input-variables at baseline, were employed to differentiate specific patient profiles. Results DEC resulted in 5 and MFAC in 3 distinct patient phenotypes. Both identified a “comorbid” cluster with higher body mass index (BMI), relevant burden of comorbidity and low levels of physical activity. Both methods also identified a younger and physically more active cluster and an elderly cluster with functional limitations, but low disease impact. The additional two clusters identified with DEC were subgroups of the young/physically active and the elderly/physically inactive clusters. Overall pain trajectories over 9 years were stable, only the numeric rating scale (NRS) for pain showed distinct increase, while physical activity decreased in all clusters. Clusters showed different (though non-significant) trajectories of joint space changes over the follow-up period of 8 years. Conclusion Two different clustering approaches yielded similar patient allocations primarily separating complex “comorbid” patients from healthier subjects, the latter divided in young/physically active vs elderly/physically inactive subjects. The observed association to clinical (pain/physical activity) and structural progression could be helpful for early trial design as strategy to enrich for patients who may specifically benefit from disease-modifying treatments