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      Anorexia nervosa

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          Abstract

          Anorexia nervosa (AN) is a psychiatric condition characterized by severe weight loss and secondary problems associated with malnutrition. AN predominantly develops in adolescence in the peripubertal period. Without early effective treatment, the course is protracted with physical, psychological and social morbidity and high mortality. Despite these effects, patients are noted to value the beliefs and behaviours that contribute to their illness rather than regarding them as problematic, which interferes with screening, prevention and early intervention. Involving the family to support interventions early in the course of the illness can produce sustained changes; however, those with a severe and/or protracted illness might require inpatient nursing support and/or outpatient psychotherapy. Prevention programmes aim to moderate the overvaluation of 'thinness' and body dissatisfaction as one of the proximal risk factors. The low prevalence of AN limits the ability to identify risk factors and to study the timing and sex distribution of the condition. However, genetic profiles, premorbid features, and brain structures and functions of patients with AN show similarities with other psychiatric disorders and contrast with obesity and metabolic disorders. Such studies are informing approaches to address the neuroadaptation to starvation and the other various physical and psychosocial deficits associated with AN. This Primer describes the epidemiology, diagnosis, screening and prevention, aetiology, treatment and quality of life of patients with AN.

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          Diagnostic and Statistical Manual of Mental Disorders

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            LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

            Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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              An Atlas of Genetic Correlations across Human Diseases and Traits

              Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
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                Author and article information

                Journal
                Nature Reviews Disease Primers
                Nat Rev Dis Primers
                Springer Science and Business Media LLC
                2056-676X
                December 17 2015
                November 26 2015
                : 1
                : 1
                Article
                10.1038/nrdp.2015.74
                27189821
                b187fe44-40ff-4ad1-ba7d-e4e6fe9fe955
                © 2015

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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