Winfried Rief

Bio: Winfried Rief is an academic researcher from University of Marburg. The author has contributed to research in topics: Anxiety & Medicine. The author has an hindex of 80, co-authored 638 publications receiving 29572 citations. Previous affiliations of Winfried Rief include University of Copenhagen & University of Mainz.

Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Abstract: Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and similar to 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 x 10(-8)), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Abstract: Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and approximately 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 x 10(-)(8)), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

A classification of chronic pain for ICD-11.

Abstract: Chronic pain has been recognized as pain that persists past normal healing time5 and hence lacks the acute warning function of physiological nociception.35 Usually pain is regarded as chronic when it lasts or recurs for more than 3 to 6 months.29 Chronic pain is a frequent condition, affecting an estimated 20% of people worldwide6,13,14,18 and accounting for 15% to 20% of physician visits.25,28 Chronic pain should receive greater attention as a global health priority because adequate pain treatment is a human right, and it is the duty of any health care system to provide it.4,13 The current version of the International Classification of Diseases (ICD) of the World Health Organization (WHO) includes some diagnostic codes for chronic pain conditions, but these diagnoses do not reflect the actual epidemiology of chronic pain, nor are they categorized in a systematic manner. The ICD is the preeminent tool for coding diagnoses and documenting investigations or therapeutic measures within the health care systems of many countries. In addition, ICD codes are commonly used to report target diseases and comorbidities of participants in clinical research. Consequently, the current lack of adequate coding in the ICD makes the acquisition of accurate epidemiological data related to chronic pain difficult, prevents adequate billing for health care expenses related to pain treatment, and hinders the development and implementation of new therapies.10,11,16,23,27,31,37 Responding to these shortcomings, the International Association for the Study of Pain (IASP) contacted the WHO and established a Task Force for the Classification of Chronic Pain. The IASP Task Force, which comprises pain experts from across the globe,19 has developed a new and pragmatic classification of chronic pain for the upcoming 11th revision of the ICD. The goal is to create a classification system that is applicable in primary care and in clinical settings for specialized pain management. A major challenge in this process was finding a rational principle of classification that suits the different types of chronic pain and fits into the general ICD-11 framework. Pain categories are variably defined based on the perceived location (headache), etiology (cancer pain), or the primarily affected anatomical system (neuropathic pain). Some diagnoses of pain defy these classification principles (fibromyalgia). This problem is not unique to the classification of pain, but exists throughout the ICD. The IASP Task Force decided to give first priority to pain etiology, followed by underlying pathophysiological mechanisms, and finally the body site. Developing this multilayered classification was greatly facilitated by a novel principle of assigning diagnostic codes in ICD-11, termed “multiple parenting.” Multiple parenting allows the same diagnosis to be subsumed under more than 1 category (for a glossary of ICD terms refer to Table ​Table1).1). Each diagnosis retains 1 category as primary parent, but is cross-referenced to other categories that function as secondary parents. Table 1 Glossary of ICD-11 terms. The new ICD category for “Chronic Pain” comprises the most common clinically relevant disorders. These disorders were divided into 7 groups (Fig. ​(Fig.1):1): (1) chronic primary pain, (2) chronic cancer pain, (3) chronic posttraumatic and postsurgical pain, (4) chronic neuropathic pain, (5) chronic headache and orofacial pain, (6) chronic visceral pain, and (7) chronic musculoskeletal pain. Experts assigned to each group are responsible for the definition of diagnostic criteria and the selection of the diagnoses to be included under these subcategories of chronic pain. Thanks to Bedirhan Ustun and Robert Jakob of the WHO, these pain diagnoses are now integrated in the beta version of ICD-11 (http://id.who.int/icd/entity/1581976053). The Task Force is generating content models for single entities to describe their clinical characteristics. After peer review overseen by the WHO Steering Committee,39 the classification of chronic pain will be voted into action by the World Health Assembly in 2017. Figure 1 Organizational chart of Task Force, IASP, and WHO interactions. The IASP Task Force was created by the IASP council and its scope defined in direct consultation of the chairs (R.D.T. and W.R.) with WHO representatives in 2012. The Task Force reports to ... 2. Classification of chronic pain Chronic pain was defined as persistent or recurrent pain lasting longer than 3 months. This definition according to pain duration has the advantage that it is clear and operationalized. Optional specifiers for each diagnosis record evidence of psychosocial factors and the severity of the pain. Pain severity can be graded based on pain intensity, pain-related distress, and functional impairment. 2.1. Chronic primary pain Chronic primary pain is pain in 1 or more anatomic regions that persists or recurs for longer than 3 months and is associated with significant emotional distress or significant functional disability (interference with activities of daily life and participation in social roles) and that cannot be better explained by another chronic pain condition. This is a new phenomenological definition, created because the etiology is unknown for many forms of chronic pain. Common conditions such as, eg, back pain that is neither identified as musculoskeletal or neuropathic pain, chronic widespread pain, fibromyalgia, and irritable bowel syndrome will be found in this section and biological findings contributing to the pain problem may or may not be present. The term “primary pain” was chosen in close liaison with the ICD-11 revision committee, who felt this was the most widely acceptable term, in particular, from a nonspecialist perspective.

Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11).

Abstract: Chronic pain is a major source of suffering. It interferes with daily functioning and often is accompanied by distress. Yet, in the International Classification of Diseases, chronic pain diagnoses are not represented systematically. The lack of appropriate codes renders accurate epidemiological investigations difficult and impedes health policy decisions regarding chronic pain such as adequate financing of access to multimodal pain management. In cooperation with the WHO, an IASP Working Group has developed a classification system that is applicable in a wide range of contexts, including pain medicine, primary care, and low-resource environments. Chronic pain is defined as pain that persists or recurs for more than 3 months. In chronic pain syndromes, pain can be the sole or a leading complaint and requires special treatment and care. In conditions such as fibromyalgia or nonspecific low-back pain, chronic pain may be conceived as a disease in its own right; in our proposal, we call this subgroup "chronic primary pain." In 6 other subgroups, pain is secondary to an underlying disease: chronic cancer-related pain, chronic neuropathic pain, chronic secondary visceral pain, chronic posttraumatic and postsurgical pain, chronic secondary headache and orofacial pain, and chronic secondary musculoskeletal pain. These conditions are summarized as "chronic secondary pain" where pain may at least initially be conceived as a symptom. Implementation of these codes in the upcoming 11th edition of International Classification of Diseases will lead to improved classification and diagnostic coding, thereby advancing the recognition of chronic pain as a health condition in its own right.

Using Multivariate Statistics

Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Pharmacological interventions for somatoform disorders in adults.

Abstract: BACKGROUND: Somatoform disorders are characterised by chronic, medically unexplained physical symptoms (MUPS). Although different medications are part of treatment routines for people with somatoform disorders in clinics and private practices, there exists no systematic review or meta-analysis on the efficacy and tolerability of these medications. We aimed to synthesise to improve optimal treatment decisions.OBJECTIVES: To assess the effects of pharmacological interventions for somatoform disorders (specifically somatisation disorder, undifferentiated somatoform disorder, somatoform autonomic dysfunction, and pain disorder) in adults.SEARCH METHODS: We searched the Cochrane Depression, Anxiety and Neurosis Review Group's Specialised Register (CCDANCTR) (to 17 January 2014). This register includes relevant randomised controlled trials (RCTs) from The Cochrane Library (all years), MEDLINE (1950 to date), EMBASE (1974 to date), and PsycINFO (1967 to date). To identify ongoing trials, we searched ClinicalTrials.gov, Current Controlled Trials metaRegister, the World Health Organization International Clinical Trials Registry Platform, and the Chinese Clinical Trials Registry. For grey literature, we searched ProQuest Dissertation {\&} Theses Database, OpenGrey, and BIOSIS Previews. We handsearched conference proceedings and reference lists of potentially relevant papers and systematic reviews and contacted experts in the field.SELECTION CRITERIA: We selected RCTs or cluster RCTs of pharmacological interventions versus placebo, treatment as usual, another medication, or a combination of different medications for somatoform disorders in adults. We included people fulfilling standardised diagnostic criteria for somatisation disorder, undifferentiated somatoform disorder, somatoform autonomic dysfunction, or somatoform pain disorder.DATA COLLECTION AND ANALYSIS: One review author and one research assistant independently extracted data and assessed risk of bias. Primary outcomes included the severity of MUPS on a continuous measure, and acceptability of treatment.MAIN RESULTS: We included 26 RCTs (33 reports), with 2159 participants, in the review. They examined the efficacy of different types of antidepressants, the combination of an antidepressant and an antipsychotic, antipsychotics alone, or natural products (NPs). The duration of the studies ranged between two and 12 weeks.One meta-analysis of placebo-controlled studies showed no clear evidence of a significant difference between tricyclic antidepressants (TCAs) and placebo for the outcome severity of MUPS (SMD -0.13; 95{\%} CI -0.39 to 0.13; 2 studies, 239 participants; I(2) = 2{\%}; low-quality evidence). For new-generation antidepressants (NGAs), there was very low-quality evidence showing they were effective in reducing the severity of MUPS (SMD -0.91; 95{\%} CI -1.36 to -0.46; 3 studies, 243 participants; I(2) = 63{\%}). For NPs there was low-quality evidence that they were effective in reducing the severity of MUPS (SMD -0.74; 95{\%} CI -0.97 to -0.51; 2 studies, 322 participants; I(2) = 0{\%}).One meta-analysis showed no clear evidence of a difference between TCAs and NGAs for severity of MUPS (SMD -0.16; 95{\%} CI -0.55 to 0.23; 3 studies, 177 participants; I(2) = 42{\%}; low-quality evidence). There was also no difference between NGAs and other NGAs for severity of MUPS (SMD -0.16; 95{\%} CI -0.45 to 0.14; 4 studies, 182 participants; I(2) = 0{\%}).Finally, one meta-analysis comparing selective serotonin reuptake inhibitors (SSRIs) with a combination of SSRIs and antipsychotics showed low-quality evidence in favour of combined treatment for severity of MUPS (SMD 0.77; 95{\%} CI 0.32 to 1.22; 2 studies, 107 participants; I(2) = 23{\%}).Differences regarding the acceptability of the treatment (rate of all-cause drop-outs) were neither found between NGAs and placebo (RR 1.01, 95{\%} CI 0.64 to 1.61; 2 studies, 163 participants; I(2) = 0{\%}; low-quality evidence) or NPs and placebo (RR 0.85, 95{\%} CI 0.40 to 1.78; 3 studies, 506 participants; I(2) = 0{\%}; low-quality evidence); nor between TCAs and other medication (RR 1.48, 95{\%} CI 0.59 to 3.72; 8 studies, 556 participants; I(2) =14{\%}; low-quality evidence); nor between antidepressants and the combination of an antidepressant and an antipsychotic (RR 0.80, 95{\%} CI 0.25 to 2.52; 2 studies, 118 participants; I(2) = 0{\%}; low-quality evidence). Percental attrition rates due to adverse effects were high in all antidepressant treatments (0{\%} to 32{\%}), but low for NPs (0{\%} to 1.7{\%}).The risk of bias was high in many domains across studies. Seventeen trials (65.4{\%}) gave no information about random sequence generation and only two (7.7{\%}) provided information about allocation concealment. Eighteen studies (69.2{\%}) revealed a high or unclear risk in blinding participants and study personnel; 23 studies had high risk of bias relating to blinding assessors. For the comparison NGA versus placebo, there was relatively high imprecision and heterogeneity due to one outlier study. Although we identified 26 studies, each comparison only contained a few studies and small numbers of participants so the results were imprecise.AUTHORS' CONCLUSIONS: The current review found very low-quality evidence for NGAs and low-quality evidence for NPs being effective in treating somatoform symptoms in adults when compared with placebo. There was some evidence that different classes of antidepressants did not differ in efficacy; however, this was limited and of low to very low quality. These results had serious shortcomings such as the high risk of bias, strong heterogeneity in the data, and small sample sizes. Furthermore, the significant effects of antidepressant treatment have to be balanced against the relatively high rates of adverse effects. Adverse effects produced by medication can have amplifying effects on symptom perceptions, particularly in people focusing on somatic symptoms without medical causes. We can only draw conclusions about short-term efficacy of the pharmacological interventions because no trial included follow-up assessments. For each of the comparisons where there were available data on acceptability rates (NGAs versus placebo, NPs versus placebo, TCAs versus other medication, and antidepressants versus a combination of an antidepressant and an antipsychotic), no clear differences between the intervention and comparator were found.Future high-quality research should be carried out to determine the effectiveness of medications other than antidepressants, to compare antidepressants more thoroughly, and to follow-up participants over longer periods (the longest follow up was just 12 weeks). Another idea for future research would be to include other outcomes such as functional impairment or dysfunctional behaviours and cognitions as well as the classical outcomes such as symptom severity, depression, or anxiety.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition

Abstract: The 3rd edition of the International Classification of Headache Disorders (ICHD-3) may be reproduced freely for scientific, educational or clinical uses by institutions, societies or individuals. Otherwise, copyright belongs exclusively to the International Headache Society. Reproduction of any part or parts in any manner for commercial uses requires the Society’s permission, which will be granted on payment of a fee. Please contact the publisher at the address below. International Headache Society 2013–2018. Applications for copyright permissions should be submitted to Sage Publications Ltd, 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP, United Kingdom (tel: þ44 (0) 207 324 8500; fax: þ44 (0) 207 324 8600; permissions@sagepub.co.uk) (www.uk.sagepub.com). Translations

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Abstract: March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee