Alfredsson, Lars

ABSTRACT Bipolar disorder’s (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder—and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic–GABAergic gradient along the psychotic factor. BD’s genetic architecture appears hierarchical—a general liability resolving into dimensions of course and comorbidity beyond diagnostic subtypes.

Objective To determine the association of allergic conditions with incident rheumatoid arthritis (RA), especially in relation to smoking history and anti-citrullinated peptide antibody (ACPA) status. Methods This case–control study included 3515 incident RA cases and 5429 matched controls from the Epidemiological Investigation of Rheumatoid Arthritis study 1995 to 2016, including questionnaire-based information on eight allergic conditions composed from a list of 59 unique allergies. We used logistic regression and adjusted ORs (aOR) to assess the association between allergic conditions and risk of RA, adjusting for age, sex, residential area, body mass index, education, and smoking, and stratified by smoking and ACPA. Results A history of any reported allergy was equally common in RA (n=1047, 30%) as among population controls (n=1540, 29%), aOR 1.04, 95% CI 0.95 to 1.15. Metal, respiratory, food, plant/pollen and chemical allergies were not associated with risk of RA. By contrast, statistically significant associations were observed for animal dander allergy (6% vs 5%, aOR 1.37, 95% CI 1.03 to 1.82), especially in ACPA-positive RA (aOR 1.46 95% CI 1.06 to 2.01) and for atopic dermatitis, in particular for older and ACPA-negative RA (aOR 2.33, 95% CI 1.37 to 3.96 at age 80). Never smokers with allergic rhinitis also had increased risk of developing RA (aOR 1.30, 95% CI 1.00 to 1.68). Conclusion Most common allergies do not increase risk of RA, nor do they protect against RA. However, some allergic conditions, notably animal dander allergy, atopic dermatitis and allergic rhinitis, were associated with an increased risk for RA.

Objective The link and interplay between different airway exposures and rheumatoid arthritis (RA) risk are unclear. This study was undertaken to determine whether respiratory disease is associated with development of RA, and specifically to examine this relationship by RA serostatus and smoking exposure. Methods Using data from the Epidemiological Investigation of Rheumatoid Arthritis study, this analysis included 1,631 incident RA cases and 3,283 matched controls recruited from 2006 to 2016. Linking these individuals to the National Patient Register provided information on past acute or chronic, upper or lower respiratory disease diagnoses. For each disease group, we estimated adjusted odds ratios (OR adj ) with 95% confidence intervals (95% CI) for RA, using logistic regression models adjusted for age, sex, residential area, body mass index, and education both overall and stratified by anti–citrullinated protein antibody (ACPA)/rheumatoid factor (RF) status and by smoking status. Results Respiratory disease diagnoses were associated with risk of RA, with an OR adj of 1.2 for acute upper respiratory disease (95% CI 0.8–1.7), 1.4 for chronic upper respiratory disease (95% CI 1.1–1.9), 2.4 for acute lower respiratory disease (95% CI 1.5–3.6), and 1.6 for chronic lower respiratory disease (95% CI 1.5–3.6). These associations were present irrespective of RF or ACPA status, though the association was somewhat stronger for ACPA/RF–positive than ACPA/RF–negative RA. The association between any respiratory disease and RA was stronger for nonsmokers (OR adj 2.1 [95% CI 1.5–2.9]) than for smokers (OR adj 1.2 [95% CI 0.9–1.5]). Conclusion Respiratory diseases increase the risk for both seropositive and seronegative RA, but only among nonsmokers. These findings raise the hypothesis that smoking and airway disease are associated with RA development through partly different mechanisms.

Abstract Eating disorders and substance use disorders frequently co‐occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [ r g ], twin‐based = 0.23‐0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome‐wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance‐use‐related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism‐based genetic correlations between eating disorder‐ and substance‐use‐related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN ( r g = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating ( r g = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating ( r gs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co‐varying for major depressive disorder loci. The patterns of association between eating disorder‐ and substance‐use‐related phenotypes highlights the potentially complex and substance‐specific relationships among these behaviors.

Objective To describe the methodology used to develop new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIMs) and their major subgroups. Methods An international, multidisciplinary group of myositis experts produced a set of 93 potentially relevant variables to be tested for inclusion in the criteria. Rheumatology, dermatology, neurology and paediatric clinics worldwide collected data on 976 IIM cases (74% adults, 26% children) and 624 non-IIM comparator cases with mimicking conditions (82% adults, 18% children). The participating clinicians classified each case as IIM or non-IIM. Generally, the classification of any given patient was based on few variables, leaving remaining variables unmeasured. We investigated the strength of the association between all variables and between these and the disease status as determined by the physician. We considered three approaches: (1) a probability-score approach, (2) a sum-of-items approach criteria and (3) a classification-tree approach. Results The approaches yielded several candidate models that were scrutinised with respect to statistical performance and clinical relevance. The probability-score approach showed superior statistical performance and clinical practicability and was therefore preferred over the others. We developed a classification tree for subclassification of patients with IIM. A calculator for electronic devices, such as computers and smartphones, facilitates the use of the European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria. Conclusions The new EULAR/ACR classification criteria provide a patient’s probability of having IIM for use in clinical and research settings. The probability is based on a score obtained by summing the weights associated with a set of criteria items.

Objective To develop and validate new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIM) and their major subgroups. Methods Candidate variables were assembled from published criteria and expert opinion using consensus methodology. Data were collected from 47 rheumatology, dermatology, neurology, and pediatric clinics worldwide. Several statistical methods were utilized to derive the classification criteria. Results Based on data from 976 IIM patients (74% adults; 26% children) and 624 non‐IIM patients with mimicking conditions (82% adults; 18% children), new criteria were derived. Each item is assigned a weighted score. The total score corresponds to a probability of having IIM. Subclassification is performed using a classification tree. A probability cutoff of 55%, corresponding to a score of 5.5 (6.7 with muscle biopsy) “probable IIM,” had best sensitivity/specificity (87%/82% without biopsies, 93%/88% with biopsies) and is recommended as a minimum to classify a patient as having IIM. A probability of ≥90%, corresponding to a score of ≥7.5 (≥8.7 with muscle biopsy), corresponds to “definite IIM.” A probability of <50%, corresponding to a score of <5.3 (<6.5 with muscle biopsy), rules out IIM, leaving a probability of ≥50–<55% as “possible IIM.” Conclusion The European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria for IIM have been endorsed by international rheumatology, dermatology, neurology, and pediatric groups. They employ easily accessible and operationally defined elements, and have been partially validated. They allow classification of “definite,” “probable,” and “possible” IIM, in addition to the major subgroups of IIM, including juvenile IIM. They generally perform better than existing criteria.

Background: Strong evidence supports the role of both genetic and environmental factors in pediatric-onset multiple sclerosis (POMS) etiology. Objective: We comprehensively investigated the association between established major histocompatibility complex (MHC) and non-MHC adult multiple sclerosis (MS)-associated variants and susceptibility to POMS. Methods: Cases with onset <18 years ( n = 569) and controls ( n = 16,251) were included from the United States and Sweden. Adjusted logistic regression and meta-analyses were performed for individual risk variants and a weighted genetic risk score (wGRS) for non-MHC variants. Results were compared to adult MS cases ( n = 7588). Results: HLA–DRB1*15:01 was strongly associated with POMS (odds ratio (OR) meta = 2.95, p < 2.0 × 10 −16 ). Furthermore, 28 of 104 non-MHC variants studied (23%) were associated ( p < 0.05); POMS cases carried, on average, a higher burden of these 28 variants compared to adults (OR avg = 1.24 vs 1.13, respectively), though the difference was not significant. The wGRS was strongly associated with POMS (OR meta = 2.77, 95% confidence interval: 2.33, 3.32, p < 2.0 × 10 −16 ) and higher, on average, when compared to adult cases. Additional class III risk variants in the MHC region associated with POMS were revealed after accounting for HLA–DRB1*15:01 and HLA–A*02. Conclusion: Pediatric and adult MS share many genetic variants suggesting similar biological processes are present. MHC variants beyond HLA–DRB1*15:01 and HLA–A*02 are also associated with POMS.