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We studied the effects of chronic angiotensin 1-7 (Ang 1-7) treatment in an experimental model of the metabolic syndrome, i.e., rats given high-fructose/low-magnesium diet (HFrD). Rats were fed on HFrD for 24 weeks with and without Ang 1-7 (576 µg/kg/day, s.c., Alzet pumps). After 6 months, Ang 1-7–treated animals had lower body weight (−9.5%), total fat mass (detected by magnetic resonance imaging), and serum triglycerides (−51%), improved glucose tolerance, and better insulin sensitivity. Similar metabolic effects were also evident, albeit in the absence of weight loss, in rats first exposed to HFrD for 5 months and then subjected to short-term (4 weeks) treatment with Ang 1-7. Six months of Ang 1-7 treatment were associated with lower plasma renin activity (−40%) and serum aldosterone (−48%), less hepatosteatatitis, and a reduction in epididymal adipocyte volume. The marked attenuation of macrophage infiltration in white adipose tissue (WAT) was associated with reduced levels of the pP65 protein in the epididymal fat tissue, suggesting less activation of the nuclear factor-κB (NFκB) pathway in Ang 1-7–treated rats. WAT from Ang 1-7–treated rats showed reduced NADPH-stimulated superoxide production. In single muscle fibers (myofibers) harvested and grown ex vivo for 10 days, myofibers from HFrD rats gave rise to 20% less myogenic cells than the Ang 1-7–treated rats. Fully developed adipocytes were present in most HFrD myofiber cultures but entirely absent in cultures from Ang 1-7–treated rats. In summary, Ang 1-7 had an ameliorating effect on insulin resistance, hypertriglyceridemia, fatty liver, obesity, adipositis, and myogenic and adipogenic differentiation in muscle tissue in the HFrD rats.

Denosumab (DMAB) efficacy for treatment of osteoporosis was demonstrated in a pivotal trial with a reduction in vertebral and hip fractures during 3 years, and fracture risk reduction was sustained up to 10 years in an extension study. DMAB causes potent yet reversible inhibition of bone resorption. Bone density declines rapidly upon discontinuation and bone turnover markers increase above baseline in a rebound fashion. Spontaneous multiple vertebral fractures after DMAB discontinuation were recently reported. Prior treatment with bisphosphonates (BP) was postulated to decrease the risk for this alarming phenomenon. We aimed to describe our experience of fractures following DMAB withdrawal with special attention to past history of osteoporosis treatment. A phone survey of physicians engaged in bone metabolism from nine hospitals in Israel was performed. Clinical data of the patients presenting with vertebral fractures upon DMAB discontinuation were summarized and compared to the previously published cases. Nine elderly (74.2 ± 5.3 years) female patients were identified. Most patients had a prolonged prior exposure to BP (7.4 ± 3.2 years). All but one sustained osteoporotic fractures prior to DMAB initiation and their FRAX scores were high. Thirty-six vertebral fractures were identified in nine patients. Eight patients presented with multiple fractures, and most fractures were spontaneous. In line with the previous reports, the timing and severity of the fractures raise concern of DMAB discontinuation effect. Prolonged BP exposure in most of our patients challenges the protective effect hypothesis. Care providers, patients, and regulatory authorities should be aware of the possible risk of DMAB treatment interruption.

This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient‐specific supervised‐machine‐learning (SML) analysis of glucose level based on a continuous‐glucose‐monitoring system (CGM) that needs no human intervention, and minimises false‐positive alerts. The CGM data over 7 to 50 non‐consecutive days from 11 type‐1 diabetic patients aged 18 to 39 with a mean HbA1C of 7.5% ± 1.2% were analysed using four SML models. The algorithm was constructed to choose the best‐fit model for each patient. Several statistical parameters were calculated to aggregate the magnitudes of the prediction errors. The personalised solutions provided by the algorithm were effective in predicting glucose levels 30 minutes after the last measurement. The average root‐mean‐square‐error was 20.48 mg/dL and the average absolute‐mean‐error was 15.36 mg/dL when the best‐fit model was selected for each patient. Using the best‐fit‐model, the true‐positive‐hypoglycemia‐prediction‐rate was 64%, whereas the false‐positive‐ rate was 4.0%, and the false‐negative‐rate was 0.015%. Similar results were found even when only CGM samples below 70 were considered. The true‐positive‐hyperglycemia‐prediction‐rate was 61%. State‐of‐the‐art SML tools are effective in predicting the glucose level values of patients with type‐1diabetes and notifying these patients of future hypoglycemic and hyperglycemic events, thus improving glycemic control. The algorithm can be used to improve the calculation of the basal insulin rate and bolus insulin, and suitable for a closed loop “artificial pancreas” system. The algorithm provides a personalised medical solution that can successfully identify the best‐fit method for each patient.

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