Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Giant coronary artery aneurysms and myocardial fibrosis after Kawasaki disease may lead to devastating cardiovascular outcomes. We characterised the vascular and myocardial outcomes in five selected Kawasaki disease patients with a history of giant coronary artery aneurysms that completely regressed.

Five patients were selected who had giant coronary artery aneurysm in early childhood that regressed when studied 12–33 years after Kawasaki disease onset. Coronary arteries were imaged by coronary CT angiography, and coronary artery calcium volume scores were determined. We used endocardial strain measurements from CT imaging to assess myocardial regional wall function. Calprotectin and galectin-3 (gal-3) as biomarkers of inflammation and myocardial fibrosis were measured by enzyme-linked immunosorbent assay.

The five selected patients with regressed giant coronary artery aneurysms had calcium scores of zero, normal levels of calprotectin and gal-3, and normal appearance of the coronary arteries by coronary computed tomography angiography. CT strain demonstrated normal peak systolic and diastolic strain patterns in four of five patients. In one patient with a myocardial infarction at the time of Kawasaki disease diagnosis at the age of 10 months, CT strain showed altered global longitudinal strain, reduced segmental peak strain, and reduced diastolic relaxation patterns in multiple left ventricle segments.

These patients illustrate that regression of giant aneurysms after Kawasaki disease is possible with no detectable calcium, normal biomarkers of inflammation and fibrosis, and normal myocardial function. Individuals with regressed giant coronary artery aneurysm still require longitudinal surveillance to assess the durability of this favourable outcome.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Prof Mucci has been a consultant and/or advisor to or has received honoraria from Gedeon Richter Bulgaria, Janssen Pharmaceuticals, Lundbeck, Otsuka, Pfizer and Pierre Fabre.

The prevalence of psychotic experiences (PEs) is higher in low-and-middle-income-countries (LAMIC) than in high-income countries (HIC). Here, we examine whether this effect is explicable by measurement bias.

A community sample from 13 countries (N = 7141) was used to examine the measurement invariance (MI) of a frequently used self-report measure of PEs, the Community Assessment of Psychic Experiences (CAPE), in LAMIC (n = 2472) and HIC (n = 4669). The CAPE measures positive (e.g. hallucinations), negative (e.g. avolition) and depressive symptoms. MI analyses were conducted with multiple-group confirmatory factor analyses.

MI analyses showed similarities in the structure and understanding of the CAPE factors between LAMIC and HIC. Partial scalar invariance was found, allowing for latent score comparisons. Residual invariance was not found, indicating that sum score comparisons are biased. A comparison of latent scores before and after MI adjustment showed both overestimation (e.g. avolition, d = 0.03 into d = −0.42) and underestimation (e.g. magical thinking, d = −0.03 into d = 0.33) of PE in LAMIC relative to HIC. After adjusting the CAPE for MI, participants from LAMIC reported significantly higher levels on most CAPE factors but a significantly lower level of avolition.

Previous studies using sum scores to compare differences across countries are likely to be biased. The direction of the bias involves both over- and underestimation of PEs in LAMIC compared to HIC. Nevertheless, the study confirms the basic finding that PEs are more frequent in LAMIC than in HIC.

Establishing the minimum clinically important difference (MCID) in functioning and cognition is essential to the interpretation of the research and clinical work conducted in bipolar disorders (BD). The present study aimed to estimate the MCID for the Functioning Assessment Short Test (FAST) and a battery of neuropsychological tests in BD.

Anchor-based and distributive methods were used to estimate the MCID for the FAST and cognition using data from a large, multicentre, observational cohort of individuals with BD. The FAST and cognition were linked with the Clinical Global Impressions Scale-Severity (CGI-S) and Global Assessment of Functioning (GAF) using an equipercentile method. The magnitude of the standard error measurement (s.e.m.) provided another estimate of the MCID.

In total, 570 participants were followed for 2 years. Cross-sectional CGI-S and GAF scores were linked to a threshold ⩽7 on the FAST for functional remission. The MCID for the FAST equalled 8- or 9-points change from baseline using the CGI-S and GAF. One s.e.m. on the FAST corresponded to 7.6-points change from baseline. Cognitive variables insufficiently correlated with anchor variables (all ρ <0.3). One s.e.m. for cognitive variables corresponded to a range of 0.45 to 0.93-s.d. change from baseline.

These findings support the value of the estimated MCID for the FAST and cognition and may be a useful tool to evaluate cognitive and functional remediation effects and improve patient functional outcomes in BD. The CGI-S and GAF were inappropriate anchors for cognition. Further studies may use performance-based measures of functioning instead.

Antidepressants may induce manic or hypomanic episodes. The identification of predictors of antidepressant- induced mania (AIM) is essential to improve the management of bipolar disorder (BD). However, the rare studies on AIM are generally characterized by small sample sizes, varying definitions of AIM and heterogeneous groups of patients, thus leading to conflicting results.

To compare a population of AIM(+) to AIM(-) patients in order to identify specific clinical factors associated with AIM.

All 252 participants met the DSM-IV criteria for BD. Only patients who reported AIM in the 90 days after the beginning of an antidepressant (with or without a mood stabilizer) were diagnosed as AIM (+) and those without any lifetime history of AIM despite lifetime antidepressant prescription were considered AIM (-). Sociodemographic and clinical factors were collected using the DIGS, ALS, AIS BIS and WURS.

AIM(+) (N=74) and AIM(-) (N=178) patients did not differ significantly in terms of age, gender distribution, bipolar disorder duration and age of onset, ALS, BIS and WURS score. However, the rates of rapid cyclers, lifetime history of suicidal acts, alcohol use disorder and AIS score were significantly higher in the AIM(+)group. The type of polarity of onset was significantly different in both groups.

A history of rapid cycling, of suicidal acts and of alcohol use disorder could be considered as risk factors of AIM in BD. Patients with these factors could therefore be identified as a vulnerable subgroup prone to manic switch with antidepressant.

Lithium remains the gold standard of prophylactic treatment in bipolar disorder. However 10–40% of patients are not responder to lithium and there is still no operational predictive markers of lithium response. Moreover, previous studies relate some conflicting results due to the absence of an unanimous definition and evaluation of prophylactic lithium response. Our objective was to identify clinical factors associated with prophylactic lithium response assessed by Alda questionnaire that includes 6 categories of prophylactic response from no response for at least two years of treatment to no relapse for three years.

To study characteristics associated with lithium response.

All 516 participants met the DSM-IV criteria for bipolar disorder. They all received at least once in their life lithium (for at least two years) and all completed the Alda questionnaire. They were compared on several sociodemographic and clinical factors which were collected using the DIGS. Psychological dimensions were assessed with the ALS and AIS (for affective lability and instability), the BIS (for impulsivity) and the WURS (for ADHD screening) and history of childhood trauma.

Among the 516 subjects, 132 (25,6%) were lithium responders with no relapse during at least two years of treatment; 106 (20,5%) were poor lithium responders. These groups were compared for clinical, dimensional and childhood trauma characteristics, using both univariate and multivariate analyses.

Characteristics associated with lithium response may help to define personalized strategies.

Immune dysfunction is thought to play a critical role in the pathophysiology of bipolar disorder (BD). Better insight into the genetic control of innate immune responses is of importance due to possible interactions with environmental risk factors such as infectious agents, particularly early in life.

Given the importance of Toll-like receptors (TLRs) in innate immunity, we analysed the association of selected genetic variants of TLR-2 and TLR-4, both major sensors of pathogenic infectious and non-infectious structures, with BD.

Explore possible implications of the innate arm of the immune response in BD.

Genomic DNAs from 572 BD patients and 202 controls were analyzed for the distribution of polymorphisms on the TLR-2 and TLR-4 loci using TaqMan®. Associations were examined using Chi-square test.

We found that TLR-4 rs1927914 AA and rs11536891 TT genotypes were more frequent in BD patients than in controls (corrected p; pc = .02 and .02 respectively) particularly in early-onset BD (EOBD) patients (pc = .004 and .006) born during the summer season (pc = 02 and .002 respectively). We also found that TLR-2 rs3804099 TT and rs4696480 TT genotypes were significantly more prevalent in EOBD group as compared to the late-onset BD (LOBD) subset, the latter only after excluding patients with positive family history of psychiatric disorders (pc=0.024 and 0.002 respectively).

We report an association between BD and TLR-2 and TLR-4 genetic variants suggesting an important role for pathogens in disease development.

Bipolar disorders (BD) are characterized by sleep disturbances and emotional dysregulation both during acute episodes and remission periods. We hypothesized that sleep quality (SQ) and emotional reactivity (ER) defined clusters of patients with no or abnormal SQ and ER and we studied the association with functioning.

We performed a bi-dimensional cluster analysis using SQ and ER measures in a sample of 533 outpatients patients with BD (in remission or with subsyndromal mood symptoms). Clusters were compared for mood symptoms, sleep profile and functioning.

We identified three clusters of patients: C1 (normal ER and SQ, 54%), C2 (hypo-ER and low SQ, 22%) and C3 (hyper-ER and low SQ, 24%). C1 was characterized by minimal mood symptoms, better sleep profile and higher functioning than other clusters. Although highly different for ER, C2 and C3 had similar levels of subsyndromal mood symptoms as assessed using classical mood scales. When exploring sleep domains, C2 showed poor sleep efficiency and a trend for longer sleep latency as compared to C3. Interestingly, alterations in functioning were similar in C2 and C3, with no difference in any of the sub-domains.

Abnormalities in ER and SQ delineated three clusters of patients with BD and significantly impacted on functioning.

A range of endophenotypes characterise psychosis, however there has been limited work understanding if and how they are inter-related.

This multi-centre study includes 8754 participants: 2212 people with a psychotic disorder, 1487 unaffected relatives of probands, and 5055 healthy controls. We investigated cognition [digit span (N = 3127), block design (N = 5491), and the Rey Auditory Verbal Learning Test (N = 3543)], electrophysiology [P300 amplitude and latency (N = 1102)], and neuroanatomy [lateral ventricular volume (N = 1721)]. We used linear regression to assess the interrelationships between endophenotypes.

The P300 amplitude and latency were not associated (regression coef. −0.06, 95% CI −0.12 to 0.01, p = 0.060), and P300 amplitude was positively associated with block design (coef. 0.19, 95% CI 0.10–0.28, p < 0.001). There was no evidence of associations between lateral ventricular volume and the other measures (all p > 0.38). All the cognitive endophenotypes were associated with each other in the expected directions (all p < 0.001). Lastly, the relationships between pairs of endophenotypes were consistent in all three participant groups, differing for some of the cognitive pairings only in the strengths of the relationships.

The P300 amplitude and latency are independent endophenotypes; the former indexing spatial visualisation and working memory, and the latter is hypothesised to index basic processing speed. Individuals with psychotic illnesses, their unaffected relatives, and healthy controls all show similar patterns of associations between endophenotypes, endorsing the theory of a continuum of psychosis liability across the population.

Malaria elimination is on global agendas following successful transmission reductions. Nevertheless moving from low to zero transmission is challenging. South Africa has an elimination target of 2018, which may or may not be realised in its hypoendemic areas.

The Agincourt Health and Demographic Surveillance System has monitored population health in north-eastern South Africa since 1992. Malaria deaths were analysed against individual factors, socioeconomic status, labour migration and weather over a 21-year period, eliciting trends over time and associations with covariates.

Of 13 251 registered deaths over 1.58 million person-years, 1.2% were attributed to malaria. Malaria mortality rates increased from 1992 to 2013, while mean daily maximum temperature rose by 1.5 °C. Travel to endemic Mozambique became easier, and malaria mortality increased in higher socioeconomic groups. Overall, malaria mortality was significantly associated with age, socioeconomic status, labour migration and employment, yearly rainfall and higher rainfall/temperature shortly before death.

Malaria persists as a small but important cause of death in this semi-rural South African population. Detailed longitudinal population data were crucial for these analyses. The findings highlight practical political, socioeconomic and environmental difficulties that may also be encountered elsewhere in moving from low-transmission scenarios to malaria elimination.

Our understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity.

This study included 1985 individuals with schizophrenia from 17 research groups around the world contributing to the ENIGMA Schizophrenia Working Group. Cortical thickness values were obtained from T1-weighted structural brain scans using FreeSurfer. A meta-analysis across sites was conducted over effect sizes from a model predicting cortical thickness by negative symptom score (harmonized Scale for the Assessment of Negative Symptoms or Positive and Negative Syndrome Scale scores).

Meta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (βstd = −0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.

Using an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.

This study describes findings from an assessment conducted to identify perceived knowledge gaps, information needs, and research priorities among state, territorial, and local public health preparedness directors and coordinators related to public health emergency preparedness and response (PHPR). The goal of the study was to gather information that would be useful for ensuring that future funding for research and evaluation targets areas most critical for advancing public health practice.

We implemented a mixed-methods approach to identify and prioritize PHPR research questions. A web survey was sent to all state, city, and territorial health agencies funded through the Public Health Emergency Preparedness (PHEP) Cooperative Agreement program and a sample of local health departments (LHDs). Three focus groups of state and local practitioners and subject matter experts from the Centers for Disease Control and Prevention (CDC) were subsequently conducted, followed by 3 meetings of an expert panel of PHPR practitioners and CDC experts to prioritize and refine the research questions.

We identified a final list of 44 research questions that were deemed by study participants as priority topics where future research can inform PHPR programs and practice. We identified differences in perceived research priorities between PHEP awardees and LHD survey respondents; the number of research questions rated as important was greater among LHDs than among PHEP awardees (75%, n=33, compared to 24%, n=15).

The research questions identified provide insight into public health practitioners’ perceived knowledge gaps and the types of information that would be most useful for informing and advancing PHPR practice. The study also points to a higher level of information need among LHDs than among PHEP awardees. These findings are important for CDC and the PHPR research community to ensure that future research studies are responsive to practitioners’ needs and provide the information required to enhance their capacity to meet the needs of the communities and jurisdictions they serve. (Disaster Med Public Health Preparedness. 2017;11:552–561)