DQB1 locus alone explains most of the risk and protection in narcolepsy with cataplexy in Europe

STUDY OBJECTIVE Prior research has identified five common genetic variants associated with narcolepsy with cataplexy in Caucasian patients. To replicate and/or extend these findings, we have tested HLA-DQB1, the previously identified 5 variants, and 10 other potential variants in a large European sample of narcolepsy with cataplexy subjects. DESIGN Retrospective case-control study. SETTING A recent study showed that over 76% of significant genome-wide association variants lie within DNase I hypersensitive sites (DHSs). From our previous GWAS, we identified 30 single nucleotide polymorphisms (SNPs) with P < 10(-4) mapping to DHSs. Ten SNPs tagging these sites, HLADQB1, and all previously reported SNPs significantly associated with narcolepsy were tested for replication. PATIENTS AND PARTICIPANTS For GWAS, 1,261 narcolepsy patients and 1,422 HLA-DQB1*06:02-matched controls were included. For HLA study, 1,218 patients and 3,541 controls were included. MEASUREMENTS AND RESULTS None of the top variants within DHSs were replicated. Out of the five previously reported SNPs, only rs2858884 within the HLA region (P < 2x10(-9)) and rs1154155 within the TRA locus (P < 2x10(-8)) replicated. DQB1 typing confirmed that DQB1*06:02 confers an extraordinary risk (odds ratio 251). Four protective alleles (DQB1*06:03, odds ratio 0.17, DQB1*05:01, odds ratio 0.56, DQB1*06:09 odds ratio 0.21, DQB1*02 odds ratio 0.76) were also identified. CONCLUSION An overwhelming portion of genetic risk for narcolepsy with cataplexy is found at DQB1 locus. Since DQB1*06:02 positive subjects are at 251-fold increase in risk for narcolepsy, and all recent cases of narcolepsy after H1N1 vaccination are positive for this allele, DQB1 genotyping may be relevant to public health policy.

The Dark Side of the Moon: Meta-analytical Impact of Recruitment Strategies on Risk Enrichment in the Clinical High Risk State for Psychosis

Background: The individual risk of developing psychosis after being tested for clinical high-risk (CHR) criteria (posttest risk of psychosis) depends on the underlying risk of the disease of the population from which the person is selected (pretest risk of psychosis), and thus on recruitment strategies. Yet, the impact of recruitment strategies on pretest risk of psychosis is unknown. Methods: Meta-analysis of the pretest risk of psychosis in help-seeking patients selected to undergo CHR assessment: total transitions to psychosis over the pool of patients assessed for potential risk and deemed at risk (CHR+) or not at risk (CHR−). Recruitment strategies (number of outreach activities per study, main target of outreach campaign, and proportion of self-referrals) were the moderators examined in meta-regressions. Results: 11 independent studies met the inclusion criteria, for a total of 2519 (CHR+: n = 1359; CHR−: n = 1160) help-seeking patients undergoing CHR assessment (mean follow-up: 38 months). The overall meta-analytical pretest risk for psychosis in help-seeking patients was 15%, with high heterogeneity (95% CI: 9%–24%, I 2 = 96, P < .001). Recruitment strategies were heterogeneous and opportunistic. Heterogeneity was largely explained by intensive (n = 11, β = −.166, Q = 9.441, P = .002) outreach campaigns primarily targeting the general public (n = 11, β = −1.15, Q = 21.35, P < .001) along with higher proportions of self-referrals (n = 10, β = −.029, Q = 4.262, P = .039), which diluted pretest risk for psychosis in patients undergoing CHR assessment. Conclusions: There is meta-analytical evidence for overall risk enrichment (pretest risk for psychosis at 38monhts = 15%) in help-seeking samples selected for CHR assessment as compared to the general population (pretest risk of psychosis at 38monhts=0.1%). Intensive outreach campaigns predominantly targeting the general population and a higher proportion of self-referrals diluted the pretest risk for psychosis.

At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction

An accurate detection of individuals at clinical high risk (CHR) for psychosis is a prerequisite for effective preventive interventions. Several psychometric interviews are available, but their prognostic accuracy is unknown. We conducted a prognostic accuracy meta-analysis of psychometric interviews used to examine referrals to high risk services. The index test was an established CHR psychometric instrument used to identify subjects with and without CHR (CHR+ and CHR-). The reference index was psychosis onset over time in both CHR+ and CHR- subjects. Data were analyzed with MIDAS (STATA13). Area under the curve (AUC), summary receiver operating characteristic curves, quality assessment, likelihood ratios, Fagan's nomogram and probability modified plots were computed. Eleven independent studies were included, with a total of 2,519 help-seeking, predominately adult subjects (CHR+: N=1,359; CHR-: N=1,160) referred to high risk services. The mean follow-up duration was 38 months. The AUC was excellent (0.90; 95% CI: 0.87-0.93), and comparable to other tests in preventive medicine, suggesting clinical utility in subjects referred to high risk services. Meta-regression analyses revealed an effect for exposure to antipsychotics and no effects for type of instrument, age, gender, follow-up time, sample size, quality assessment, proportion of CHR+ subjects in the total sample. Fagan's nomogram indicated a low positive predictive value (5.74%) in the general non-help-seeking population. Albeit the clear need to further improve prediction of psychosis, these findings support the use of psychometric prognostic interviews for CHR as clinical tools for an indicated prevention in subjects seeking help at high risk services worldwide.

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes’ theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment.