Simvastatin therapy in higher dosages deteriorates bone quality: Consistent evidence from population-wide patient data and interventional mouse studies.

BACKGROUND: Combining mouse experiments with big data analysis of the Austrian population, we investigated the association between high-dose statin treatment and bone quality. METHODS: The bone microarchitecture of the femur and vertebral body L4 was measured in male and ovariectomized female mice on a high-fat diet containing simvastatin (1.2 g/kg). A sex-specific matched big data analysis of Austrian health insurance claims using multiple logistic regression models was conducted (simvastatin 60-80 mg/day vs. controls; males: n = 138,666; females: n = 155,055). RESULTS: High-dose simvastatin impaired bone quality in male and ovariectomized mice. In the trabecular femur, simvastatin reduced bone volume (µm3: ♂, 213 ± 15 vs. 131 ± 7, p < 0.0001; ♀, 66 ± 7 vs. 44 ± 5, p = 0.02) and trabecular number (1/mm: ♂, 1.88 ± 0.09 vs. 1.27 ± 0.06, p < 0.0001; ♀, 0.60 ± 0.05 vs. 0.43 ± 0.04, p = 0.01). In the cortical femur, bone volume (mm3: ♂, 1.44 ± 0.03 vs. 1.34 ± 0.03, p = 0.009; ♀, 1.33 ± 0.03 vs. 1.12 ± 0.03, p = 0.0002) and cortical thickness were impaired (µm: ♂, 211 ± 4 vs. 189 ± 4, p = 0.0004; ♀, 193 ± 3 vs. 169 ± 3, p < 0.0001). Similar impairments were found in vertebral body L4. Simvastatin-induced changes in weight or glucose metabolism were excluded as mediators of deteriorations in bone quality. Results from mice were supported by a matched cohort analysis showing an association between high-dose simvastatin and increased risk of osteoporosis in patients (♂, OR: 5.91, CI: 3.17-10.99, p < 0.001; ♀, OR: 4.16, CI: 2.92-5.92, p < 0.001). CONCLUSION: High-dose simvastatin dramatically reduces bone quality in obese male and ovariectomized female mice, suggesting that direct drug action accounts for the association between high dosage and increased risk of osteoporosis as observed in comparable human cohorts. The underlying pathophysiological mechanisms behind this relationship are presently unknown and require further investigation.

A systematic approach to analyse the impact of farm-profiles on bovine health.

In this study we present systematic framework to analyse the impact of farm profiles as combinations of environmental conditions and management practices on common diseases in dairy cattle. The data used for this secondary data analysis includes observational data from 166 farms with a total of 5828 dairy cows. Each farm is characterised by features from five categories: husbandry, feeding, environmental conditions, housing, and milking systems. We combine dimension reduction with clustering techniques to identify groups of similar farm attributes, which we refer to as farm profiles. A statistical analysis of the farm profiles and their related disease risks is carried out to study the associations between disease risk, farm membership to a specific cluster as well as variables that characterise a given cluster by means of a multivariate regression model. The disease risks of five different farm profiles arise as the result of complex interactions between environmental conditions and farm management practices. We confirm previously documented relationships between diseases, feeding and husbandry. Furthermore, novel associations between housing and milking systems and specific disorders like lameness and ketosis have been discovered. Our approach contributes to paving a way towards a more holistic and data-driven understanding of bovine health and its risk factors.