ObjectiveWhether HMG-CoA-reductase inhibition, the main mechanism of statins, plays a role in the pathogenesis of osteoporosis, is not entirely known so far. Consequently, this study was set out to investigate the relationship of different kinds and dosages of statins with osteoporosis, hypothesising that the inhibition of the synthesis of cholesterol could influence sex-hormones and therefore the diagnosis of osteoporosis.MethodsMedical claims data of all Austrians from 2006 to 2007 was used to identify all patients treated with statins to compute their daily defined dose averages of six different types of statins. We applied multiple logistic regression to analyse the dose-dependent risks of being diagnosed with osteoporosis for each statin individually.ResultsIn the general study population, statin treatment was associated with an overrepresentation of diagnosed osteoporosis compared with controls (OR: 3.62, 95% CI 3.55 to 3.69, p<0.01). There was a highly non-trivial dependence of statin dosage with the ORs of osteoporosis. Osteoporosis was underrepresented in low-dose statin treatment (0–10 mg per day), including lovastatin (OR: 0.39, CI 0.18 to 0.84, p<0.05), pravastatin (OR: 0.68, 95% CI 0.52 to 0.89, p<0.01), simvastatin (OR: 0.70, 95% CI 0.56 to 0.86, p<0.01) and rosuvastatin (OR: 0.69, 95% CI 0.55 to 0.87, p<0.01). However, the exceeding of the 40 mg threshold for simvastatin (OR: 1.64, 95% CI 1.31 to 2.07, p<0.01), and the exceeding of a 20 mg threshold for atorvastatin (OR: 1.78, 95% CI 1.41 to 2.23, p<0.01) and for rosuvastatin (OR: 2.04, 95% CI 1.31 to 3.18, p<0.01) was related to an overrepresentation of osteoporosis.ConclusionOur results show that the diagnosis of osteoporosis in statin-treated patients is dose-dependent. Thus, osteoporosis is underrepresented in low-dose and overrepresented in high-dose statin treatment, demonstrating the importance of future studies’ taking dose-dependency into account when investigating the relationship between statins and osteoporosis.

Despite accumulating evidence of inter and intraindividual variability in response to theta burst stimulation, it is widely believed that in therapeutic applications, repeated sessions can have a "build-up" effect that increases the response over and above that seen in a single session. However, strong evidence for this is lacking. Therefore, we examined whether daily administration of intermittent theta burst stimulation (iTBS) over the primary motor cortex induces cumulative changes in transcranial magnetic stimulation measures of cortical excitability, above the changes induced by sham stimulation. Over five consecutive days, 20 healthy participants received either active iTBS or sham stimulation. Each day, baseline measures of cortical excitability were assessed before and up to 30 min after the intervention. There was no significant difference in the rate of response between iTBS and sham stimulation on any of the 5 days. There was no iTBS specific cumulative increase of corticospinal excitability. The likelihood that an individual would remain a responder from day-to-day was low in both groups, implying high within-subject variability of both active and sham iTBS after-effects. In contrast, we found a high within-subject repeatability of resting and active motor threshold, and baseline motor-evoked potential amplitude. In summary, sham stimulation has similar effect to active iTBS on corticospinal excitability, even when applied repeatedly for 5 days. Our results might be relevant to research and clinical applications of theta burst stimulation protocols.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.