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Träfflista för sökning "WFRF:(Hilding A) srt2:(2020-2023)"

Sökning: WFRF:(Hilding A) > (2020-2023)

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1.
  • Brismar, K, et al. (författare)
  • Adiponectin, IGFBP-1 and -2 are independent predictors in forecasting prediabetes and type 2 diabetes
  • 2023
  • Ingår i: Frontiers in endocrinology. - : Frontiers Media SA. - 1664-2392. ; 13, s. 1092307-
  • Tidskriftsartikel (refereegranskat)abstract
    • Adiponectin and insulin-like growth factor (IGF) binding proteins IGFBP-1 and IGFBP-2 are biomarkers of insulin sensitivity. IGFBP-1 reflects insulin sensitivity in the liver, adiponectin in adipose tissue and IGFBP-2 in both tissues. Here, we study the power of the biomarkers adiponectin, IGFBP-1, IGFBP-2, and also included IGF-I and IGF-II, in predicting prediabetes and type 2 diabetes (T2D) in men and women with normal oral glucose tolerance (NGT).DesignSubjects with NGT (35-56 years) recruited during 1992-1998 were re-investigated 8-10 years later. In a nested case control study, subjects progressing to prediabetes (133 women, 164 men) or to T2D (55 women, 98 men) were compared with age and sex matched NGT controls (200 women and 277 men).MethodsThe evaluation included questionnaires, health status, anthropometry, biochemistry and oral glucose tolerance test.ResultsAfter adjustment, the lowest quartile of adiponectin, IGFBP-1 and IGFBP-2 associated independently with future abnormal glucose tolerance (AGT) in both genders in multivariate analyses. High IGFs predicted weakly AGT in women. In women, low IGFBP-2 was the strongest predictor for prediabetes (OR:7.5), and low adiponectin for T2D (OR:29.4). In men, low IGFBP-1 was the strongest predictor for both prediabetes (OR:13.4) and T2D (OR:14.9). When adiponectin, IGFBP-1 and IGFBP-2 were combined, the ROC-AUC reached 0.87 for women and 0.79 for men, higher than for BMI alone.ConclusionDifferences were observed comparing adipocyte- and hepatocyte-derived biomarkers in forecasting AGT in NGT subjects. In women the strongest predictor for T2D was adiponectin and in men IGFBP-1, and for prediabetes IGFBP-2 in women and IGFBP-1 in men.
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2.
  • Schierbeck, S., et al. (författare)
  • Simulation and education National coverage of out-of-hospital cardiac arrests using automated external defibrillator-equipped drones-A geographical information system analysis
  • 2021
  • Ingår i: Resuscitation. - : Elsevier BV. - 0300-9572 .- 1873-1570. ; 163, s. 136-145
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Early defibrillation is essential for increasing the chance of survival in out-of-hospital-cardiac-arrest (OHCA). Automated external defibrillator (AED)-equipped drones have a substantial potential to shorten times to defibrillation in OHCA patients. However, optimal locations for drone deployment are unknown. Our aims were to find areas of high incidence of OHCA on a national level for placement of AED-drones, and to quantify the number of drones needed to reach 50, 80, 90 and 100% of the target population within eight minutes. Methods: This is a retrospective observational study of OHCAs reported to the Swedish Registry for Cardiopulmonary Resuscitation between 2010-2018. Spatial analyses of optimal drone placement were performed using geographical information system (GIS)-analyses covering high-incidence areas (>100 OHCAs in 2010-2018) and response times. Results: 39,246 OHCAs were included. To reach all OHCAs in high-incidence areas with AEDs delivered by drone or ambulance within eight minutes, 61 drone systems would be needed, resulting in overall OHCA coverage of 58.2%, and median timesaving of 05:01 (min:sec) [IQR 03:22-06:19]. To reach 50% of the historically reported OHCAs in <8 min, 21 drone systems would be needed; for 80%, 366; for 90%, 784, and for 100%, 2408. Conclusions: At a national level, GIS-analyses can identify high incidence areas of OHCA and serve as tools to quantify the need of AED-equipped drones. Use of only a small number of drone systems can increase national coverage of OHCA substantially. Prospective real-life studies are needed to evaluate theoretically optimized suggestions for drone placement.
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