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  • 1.
    Anderbro, Therese
    et al.
    Stockholm Univ, Dept Psychol, S-10691 Stockholm, Sweden.
    Gonder-Frederick, Linda
    Univ Virginia, Dept Psychiat & Neurobehav Sci, Charlottesville, VA USA.
    Bolinder, Jan
    Karolinska Inst, Karolinska Univ Hosp, Dept Med, Huddinge, Sweden.
    Lins, Per-Eric
    Karolinska Inst, Danderyd Hosp, Dept Clin Sci, Div Med, Stockholm, Sweden.
    Wredling, Regina
    Karolinska Inst, Danderyd Hosp, Dept Clin Sci, Div Med, Stockholm, Sweden.
    Moberg, Erik
    Karolinska Inst, Karolinska Univ Hosp, Dept Med, Huddinge, Sweden.
    Lisspers, Jan
    Mid Sweden University, Faculty of Human Sciences, Department of Psychology. Sophiahemmet Univ Coll, Stockholm, Sweden.
    Johansson, Unn-Britt
    Sophiahemmet Univ Coll, Stockholm, Sweden.
    Fear of hypoglycemia: relationship to hypoglycemic risk and psychological factors2015In: Acta Diabetologica, ISSN 0940-5429, E-ISSN 1432-5233, Vol. 52, no 3, p. 581-589Article in journal (Refereed)
    Abstract [en]

    The major aims of this study were to examine (1) the association between fear of hypoglycemia (FOH) in adults with type 1 diabetes with demographic, psychological (anxiety and depression), and disease-specific clinical factors (hypoglycemia history and unawareness, A(1c)), including severe hypoglycemia (SH), and (2) differences in patient subgroups categorized by level of FOH and risk of SH. Questionnaires were mailed to 764 patients with type 1 diabetes including the Swedish translation of the Hypoglycemia Fear Survey (HFS) and other psychological measures including the Perceived Stress Scale, Hospital Anxiety and Depression Scale, Anxiety Sensitivity Index, Social Phobia Scale, and Fear of Complications Scale. A questionnaire to assess hypoglycemia history was also included and A(1c) measures were obtained from medical records. Statistical analyses included univariate approaches, multiple stepwise linear regressions, Chi-square t tests, and ANOVAs. Regressions showed that several clinical factors (SH history, frequency of nocturnal hypoglycemia, self-monitoring) were significantly associated with FOH but R (2) increased from 16.25 to 39.2 % when anxiety measures were added to the model. When patients were categorized by level of FOH (low, high) and SH risk (low, high), subgroups showed significant differences in non-diabetes-related anxiety, hypoglycemia history, self-monitoring, and glycemic control. There is a strong link between FOH and non-diabetes-related anxiety, as well as hypoglycemia history. Comparison of patient subgroups categorized according to level of FOH and SH risk demonstrated the complexity of FOH and identified important differences in psychological and clinical variables, which have implications for clinical interventions.

  • 2.
    Keildson, S.
    et al.
    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom .
    Fadista, J.
    Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Lund University, Malmö, Sweden.
    Ladenvall, C.
    Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Lund University, Malmö, Sweden.
    Hedman, A. K.
    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom .
    Elgzyri, T.
    Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Lund University, Malmö, Sweden .
    Small, K. S.
    Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom .
    Grundberg, E.
    Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom .
    Nica, A. C.
    Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland .
    Glass, D.
    Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom .
    Richards, J. B.
    Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom .
    Barrett, A.
    Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, United Kingdom .
    Nisbet, J.
    Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom .
    Zheng, H. -F
    Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Jewish General Hospital, Montreal, QC, Canada .
    Rönn, T.
    Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Lund University, Malmö, Sweden .
    Ström, Kristoffer
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Eriksson, K. -F
    Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Lund University, Malmö, Sweden .
    Prokopenko, I.
    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
    Spector, T. D.
    Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom .
    Dermitzakis, E. T.
    Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
    Deloukas, P.
    Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom .
    McCarthy, M. I.
    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom .
    Rung, J.
    European Molecular Biology Laboratory-European Bioinformatics Institute, Cambridge, United Kingdom .
    Groop, L.
    Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Lund University, Malmö, Sweden .
    Franks, P. W.
    Department of Clinical Sciences, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Lund University, Malmö, Sweden .
    Lindgren, C. M.
    Broad Institute of Massachusetts Institute of Technology, Harvard University, Cambridge, MA, USA.
    Hansson, O.
    Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Lund University, Malmö, Sweden .
    Expression of phosphofructokinase in skeletal muscle is influenced by genetic variation and associated with insulin sensitivity2014In: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 63, no 3, p. 1154-1165Article in journal (Refereed)
    Abstract [en]

    Using an integrative approach in which genetic variation, gene expression, and clinical phenotypes are assessed in relevant tissues may help functionally characterize the contribution of genetics to disease susceptibility. We sought to identify genetic variation influencing skeletal muscle gene expression (expression quantitative trait loci [eQTLs]) as well as expression associated with measures of insulin sensitivity. We investigated associations of 3,799,401 genetic variants in expression of >7,000 genes from three cohorts (n = 104). We identified 287 genes with cis-acting eQTLs (false discovery rate [FDR] <5%; P < 1.96×1025) and 49 expression-insulin sensitivity phenotype associations (i.e., fasting insulin, homeostasis model assessment-insulin resistance, and BMI) (FDR <5%; P = 1.34×1024). One of these associations, fasting insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Furthermore, the expression of PFKM, a rate-limiting enzyme in glycolysis, was nominally associated with glucose uptake in skeletal muscle (P = 0.026; n = 42) and overexpressed (Bonferroni-corrected P = 0.03) in skeletal muscle of patients with T2D (n = 102) compared with normoglycemic controls (n = 87). The PFKM eQTL (rs4547172; P = 7.69 × 1026) was nominally associated with glucose uptake, glucose oxidation rate, intramuscular triglyceride content, and metabolic flexibility (P = 0.016-0.048; n = 178). We explored eQTL results using published data from genome-wide association studies (DIAGRAM and MAGIC), and a proxy for the PFKM eQTL (rs11168327; r 2 = 0.75) was nominally associated with T2D (DIAGRAM P = 2.7×1023). Taken together, our analysis highlights PFKM as a potential regulator of skeletal muscle insulin sensitivity. © 2014 by the American Diabetes Association..

  • 3.
    Planck, Tereza
    et al.
    Department of Clinical Sciences, Diabetes and Endocrinology, CRC, Skane University Hospital, Jan Waldenströms gata 35, Malmö, Sweden.
    Shahida, Bushra
    Department of Clinical Sciences Malmö, Lunds Universitet, Lund, Sweden.
    Parikh, Hemang
    YouGenomics LLC, Germantown, MD, United States .
    Ström, Kristoffer
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. YouGenomics LLC, Germantown, MD, United States .
    Asman, Peter
    Department of Clinical Sciences Malmö, Lunds Universitet, Lund, Sweden .
    Brorson, Hakan
    Department of Clinical Sciences Malmö, Lunds Universitet, Lund, Sweden .
    Hallengren, Bengt
    Department of Clinical Sciences Malmö, Lunds Universitet, Lund, Sweden .
    Lantz, Mikael
    Department of Clinical Sciences Malmö, Lunds Universitet, Lund, Sweden .
    Smoking Induces Overexpression of Immediate Early Genes in Active Graves' Ophthalmopathy2014In: Thyroid, ISSN 1050-7256, E-ISSN 1557-9077, Vol. 24, no 10, p. 1524-1532Article in journal (Refereed)
    Abstract [en]

    Background: Cigarette smoking is a risk factor for the development of Graves' ophthalmopathy (GO). In a previous study of gene expression in intraorbital fat, adipocyte-related immediate early genes (IEGs) were overexpressed in patients with GO compared to controls. We investigated whether IEGs are upregulated by smoking, and examined other pathways that may be affected by smoking. Methods: Gene expression in intraorbital fat was studied in smokers (n=8) and nonsmokers (n=8) with severe active GO, as well as in subcutaneous fat in thyroid-healthy smokers (n=5) and nonsmokers (n=5) using microarray and real-time polymerase chain reaction (PCR). Results: With microarray, eight IEGs were upregulated more than 1.5-fold in smokers compared to nonsmokers with GO. Five were chosen for confirmation and were also overexpressed with real-time PCR. Interleukin-1 beta/IL-1B/(2.3-fold) and interleukin-6/IL-6/(2.4-fold) were upregulated both with microarray and with real-time PCR in smokers with GO compared to nonsmokers. Major histocompatibility complex, class II, DR beta 1/HLA-DRB1/was upregulated with microarray (2.1-fold) and with borderline significance with real-time PCR. None of these genes were upregulated in smokers compared to nonsmokers in subcutaneous fat. Conclusions: IEGs, IL-1B, and IL-6 were overexpressed in smokers with severe active GO compared to nonsmokers, suggesting that smoking activates pathways associated with adipogenesis and inflammation. This study underlines the importance of IEGs in the pathogenesis of GO, and provides evidence for possible novel therapeutic interventions in GO. The mechanisms activated by smoking may be shared with other conditions such as rheumatoid arthritis.

  • 4.
    Sjörs, A.
    et al.
    Institute of Stress Medicine, Gothenburg, Sweden.
    Ljung, Thomas
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Jonsdottir, I. H.
    Institute of Stress Medicine, Gothenburg, Sweden.
    Long-term follow-up of cortisol awakening response in patients treated for stress-related exhaustion2012In: BMJ Open, ISSN 2044-6055, Vol. 2, no 4, p. Art. no. 001091-Article in journal (Refereed)
    Abstract [en]

    Objectives: Studies on hypothalamus - pituitarye - adrenal (HPA) axis activity in stress-related exhaustion and burnout have revealed incongruent results, and few longitudinal studies on clinical populations have been performed. This study was designed to investigate differences in HPA axis activity between patients with stress-related exhaustion and healthy controls and to investigate longitudinal changes in HPA axis activity in the patient group as they entered a multimodal treatment programme. Design: HPA axis activity was assessed through the cortisol awakening response (CAR). Salivary cortisol was sampled at awakening and after 15 min. Follow-up measurements were performed in the patient group after 3, 6, 12 and 18 months. Setting: An outpatient clinic specialising in stress-related illness. Participants: Patients with clinically diagnosed stress-related exhaustion (n=162) and healthy controls (n=79). Primary and secondary outcome measures: The primary measure was CAR measured as the difference between the two salivary cortisol samples. Changes in CAR during follow-up were related to changes in symptoms of burnout, depression and anxiety. Results: Patients showed similar CAR as the controls and their CAR did not change significantly during treatment. No association was found between CAR and symptom development during treatment. Conclusions: The authors conclude that CAR does not seem to discriminate clinically defined patients with exhaustion from healthy controls and it appears not to change during treatment. CAR, measured as salivary cortisol, at awakening and after 15 min, is thus not a valid marker for stress-related exhaustion.

1 - 4 of 4
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