The Phenome-wide Consequences of Anorexia Nervosa Genes

Johnson, Jessica; Cote, Alanna C.; Dobbyn, Amanda; Sloofman, Laura; Xu, Jiayi; Cotter, Liam; Charney, Alexander; Jordan, Jennifer; Kennedy, Martin A.; Landén, Mikael; Maguire, Sarah; Martin, Nicholas G.; Mortensen, Preben Bo; Bulik, Cynthia M.; Huckins, Laura

doi:10.1101/2021.02.12.21250941

Cited by 6 publications

(3 citation statements)

References 95 publications

(103 reference statements)

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“…A PheWAS functionally queries an electronic health record (EHR) to identify associations of a trait of interest (i.e., AN) with the clinical phenome (i.e., all of the disease and medication codes included in the EHR). Exploring the phenotypic associations with AN genetic architecture can isolate how GWAS variants functionally contribute to AN disease risk, symptomatology, and clinical presentation [19]. Several PheWAS of AN are currently underway.…”

Section: The Endocrinological and Metabolic Setting Of Anmentioning

confidence: 99%

Reframing anorexia nervosa as a metabo-psychiatric disorder

Bulik

Carroll

Mehler

2021

Trends in Endocrinology & Metabolism

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Anorexia nervosa (AN) is a serious and often fatal illness. Despite decades of research, investigators have failed to adequately advance our understanding of the biological aspects of AN that could inform the development of effective interventions. Genome-wide association studies are revealing the important role of metabolic factors in AN, and studies of the gastrointestinal tract are shedding light on disruptions in enteric microbial communities and anomalies in gut morphology. In this opinion piece, we review the state of the science through the lens of the clinical presentation of illness. We project how the integration of rigorous science in genomics and microbiology, in collaboration with experienced clinicians, has the potential to markedly enhance treatment outcome via precision interventions. Shifting paradigms in understanding and treating ANAN rests squarely on the psyche-soma border; however, etiological theories and treatments have historically focused on psychological, family, and societal factors. A conceptual shift is underway. The latest genome-wide association study (GWAS; see Glossary) for AN [1] suggested reconceptualizing AN as a 'metabo-psychiatric disorder'. In addition to identifying eight significantly associated genomic loci, a rich panel of genetic correlations underscored the role of psychiatric, anthropometric, and metabolic factors in the etiology of this highly lethal disease.

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Section: The Endocrinological and Metabolic Setting Of Anmentioning

confidence: 99%

Reframing anorexia nervosa as a metabo-psychiatric disorder

Bulik

Carroll

Mehler

2021

Trends in Endocrinology & Metabolism

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“…However, it is still possible that there are undiagnosed patients included in the analysis as the BioMe TM Biobank. For example, an individual may not have a EHR record of AN in the BioMe TM Biobank if (s)he had an ANrelated visit at another healthcare facility or when they were adolescents 7 (the youngest individual included in this study is 25 years old). Given the low prevalence of AN (~1%) 41 and our exclusion of all individuals with any ED diagnosis, we expect the impact of diagnostic contamination should be minimal.…”

Section: Discussionmentioning

confidence: 99%

Exploring the clinical consequences and genetic aetiology of adult weight trajectories

Johnson

Birgegård

et al. 2021

Preprint

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Background: Longitudinal weight trajectories may reflect individual health status. We examined the genetic aetiology and clinical consequences of adult weight trajectories in males and females leveraging genetic and phenotypic data in the electronic health records (EHR) of the BioMeTM Biobank. Methods: We constructed four longitudinal weight trajectories using annual EHR-recorded weights (stable weight, weight gain, weight loss, or weight cycle) (n=21,487). After validating the accuracy of the trajectories (n=100), we conducted a hypothesis-free phenome-wide association study (PheWAS), including sex-stratified PheWAS, to identify diseases associated with each weight trajectory. We then performed a hypothesis-driven polygenic risk score (PRS) analysis on these weight trajectories, focusing on anorexia nervosa (AN) and depression—both commonly associated with weight changes. Findings: Weight trajectory classification was highly accurate (accuracy, sensitivity, and specificity > 97% for all four trajectories). Hypothesis-free PheWAS analyses identified a significant association between depression and weight cycle (OR=1.4, p≤7.7×10-16) after Bonferroni correction, but not with weight gain or loss. Compared to other weight trajectories, we also observed a significant association of osteoporosis-related phecodes with weight loss in females only (ORfemale=1.4, pfemale≤1.4×10-7, ORmale=0.8, pmale≥0.18). AN-PRS was positively associated with weight loss trajectory among individuals without eating disorder diagnoses (ORtop vs. bottom 10% PRS=1.95, p=0.00035). Consistent effect direction was observed across three ancestry groups. The AN-PRS-weight loss association was not attenuated by obesity-PRS (ORtop vs. bottom 10% PRS=1.94). Interpretation: Adult weight trajectory is associated with disease both phenotypically and genetically. Our PheWAS reveals unique relationships between diseases and weight trajectory patterns, including the association of depression and weight cycle trajectory in both males and females, and osteoporosis-weight loss trajectory association in females only. In addition, our PRS analysis suggests that adults with higher AN genetic risk are more likely to have a weight loss trajectory, and this association may be independent of BMI/obesity-related genetic pathways.

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“…Previous clinical & epidemiological studies have found a range of psychiatric (e.g., stress, depression, trauma, anxiety 26,27 ), lifestyle (e.g., smoking, 28,29 sedentary behaviour 30,31 ) and socioeconomic factors (e.g., income & education levels 32,33 ) associated with an increased risk of chronic pain, while medical conditions such as obesity 34 & cardiovascular diseases 35,36 are also common comorbidities. 37 Large-scale GWAS have identified specific genetic loci in chronic pain conditions (e.g., back, 12,19 neck/shoulder, 15 knee pain 9 & widespread pain 14,20 ) and demonstrated shared genetic bases with biopsychosocial traits (e.g., depression, 19,20,27 post-traumatic stress disorder, 20 anorexia nervosa, 38 asthma, 20 brain morphology, 18 poor sleep, 8,19,39 obesity, 5 low educational attainment 19,40 & smoking 19,40 ). However, the nature of causal relationships between common chronic pain conditions and such multidimensional factors remains to be comprehensively examined.…”

Section: Introductionmentioning

confidence: 99%

A shared genetic signature for common chronic pain conditions and its impact on biopsychosocial traits

Farrell

Kho

Campos

et al. 2022

Preprint

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Background: The multifactorial heterogenous nature of chronic pain with its multiple comorbidities presents a formidable challenge in disentangling the aetiology underlying patient symptoms. Methods: Here, we performed genome-wide association studies (GWAS) of eight types of regional chronic pain using UK Biobank data (N=4,037-79,089 cases; N=239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. Findings: We report evidence of a shared genetic signature in common regional chronic pain types, with their genetic correlations and causal directions being broadly consistent across a wide array of biopsychosocial traits. Furthermore, we identified 5,942 significant genetic correlations, of which 570 trait pairs showed evidence of a likely causal association (|GCP| > 0.6; 5% false discovery rate), including 488 traits contributing to chronic pain while 82 were affected by pain. A range of somatic pathologies (e.g., musculoskeletal, visceral), psychiatric factors (e.g., depression, trauma, anxiety, mania, bipolar disorder), socioeconomic factors (e.g., occupation) and other medical comorbidities (e.g., cardiovascular disease) contributed to an increased risk of regional chronic pain. Conversely, each chronic pain type contributed to various other traits such as increased medication use (e.g., analgesics) and risk of ischaemic heart disease and depression. Interpretation: Findings of this data-driven study, through comprehensive analysis of a vast range of biopsychosocial factors, are indicative of a common genetic signature underlying eight regional chronic pain types. We identify a broad range of traits with genetic causal effects upon chronic pain, which may inform development of novel diagnostic and therapeutic strategies, as well as provide convergent support for various existing approaches.

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The Phenome-wide Consequences of Anorexia Nervosa Genes

Cited by 6 publications

References 95 publications

Reframing anorexia nervosa as a metabo-psychiatric disorder

Reframing anorexia nervosa as a metabo-psychiatric disorder

Exploring the clinical consequences and genetic aetiology of adult weight trajectories

A shared genetic signature for common chronic pain conditions and its impact on biopsychosocial traits

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