Testing equivalence of two doses of intravenous iron to treat iron deficiency in pregnancy: A randomised controlled trial

OBJECTIVE

To test equivalence of two doses of intravenous iron (ferric carboxymaltose) in pregnancy.

DESIGN

Parallel, two-arm equivalence randomised controlled trial with an equivalence margin of 5%.

SETTING

Single centre in Australia.

POPULATION

278 pregnant women with iron deficiency.

METHODS

Participants received either 500 mg (n=152) or 1000mg (n=126) of intravenous ferric carboxymaltose in the second or third trimester.

MAIN OUTCOME MEASURES

The proportion of participants requiring additional intravenous iron (500mg) to achieve and maintain ferritin >30ug/L (diagnostic threshold for iron deficiency) at 4 weeks post-infusion, and at 6 weeks, and 3-, 6- and 12-months postpartum. Secondary endpoints included repeat infusion rate, iron status, birth, and safety outcomes.

RESULTS

The two doses were not equivalent within a 5% margin at any timepoint. At 4 weeks post infusion, 26/73 (36%) participants required a repeat infusion in the 500 mg group compared with 5/67 (8%) in the 1000 mg group (difference in proportions, 0.283 95% confidence interval (0.177, 0.389)). Overall, participants in the 500 mg arm received twice the repeat infusion rate (0.81 (SD= 0.824 vs 0.40 (SD= 0.69), rate ratio 2.05, 95% CI (1.45, 2.91)).

CONCLUSIONS

Administration of 1000 mg ferric carboxymaltose in pregnancy maintains iron stores and reduces the need for repeat infusions. A 500 mg dose requires ongoing monitoring to ensure adequate iron stores are reached and sustained.

International Consortium on the Genetics of Electroconvulsive Therapy and Severe Depressive Disorders (Gen-ECT-ic)

Recent genome-wide association studies have demonstrated that the genetic burden associated with depression correlates with depression severity. Therefore, conducting genetic studies of patients at the most severe end of the depressive disorder spectrum, those with treatment-resistant depression and who are prescribed electroconvulsive therapy (ECT), could lead to a better understanding of the genetic underpinnings of depression. Despite ECT being one of the most effective forms of treatment for severe depressive disorders, it is usually placed at the end of treatment algorithms of current guidelines. This is perhaps because ECT has controlled risk and logistical demands including use of general anaesthesia and muscle relaxants and side-effects such as short-term memory impairment. Better understanding of the genetics and biology of ECT response and of cognitive side-effects could lead to more personalized treatment decisions. To enhance the understanding of the genomics of severe depression and ECT response, researchers and ECT providers from around the world and from various depression or ECT networks, but not limited to, such as the Psychiatric Genomics Consortium, the Clinical Alliance and Research in ECT, and the National Network of Depression Centers have formed the Genetics of ECT International Consortium (Gen-ECT-ic). Gen-ECT-ic will organize the largest clinical and genetic collection to date to study the genomics of severe depressive disorders and response to ECT, aiming for 30,000 patients worldwide using a GWAS approach. At this stage it will be the largest genomic study on treatment response in depression. Retrospective data abstraction and prospective data collection will be facilitated by a uniform data collection approach that is flexible and will incorporate data from many clinical practices. Gen-ECT-ic invites all ECT providers and researchers to join its efforts.

The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies

Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases 'type 2 diabetes, coronary artery disease, hypertension' and/or for the risk factors 'blood pressure, obesity, plasma lipid levels, insulin and glucose related traits'. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and 'depression' or 'depressive disorder' or 'depressive symptoms' or 'bipolar disorder' or 'lithium treatment response in bipolar disorder', or 'serotonin reuptake inhibitors treatment response in major depression'. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.

Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers

Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview.

Hypothesis review: are clathrin-mediated endocytosis and clathrin-dependent membrane and protein trafficking core pathophysiological processes in schizophrenia and bipolar disorder?

Clathrin-mediated endocytosis (CME) is the best-characterized mechanism governing cellular membrane and protein trafficking. In this hypothesis review, we integrate recent evidence implicating CME and related cellular trafficking mechanisms in the pathophysiology of psychotic disorders such as schizophrenia and bipolar disorder. The evidence includes proteomic and genomic findings implicating proteins and genes of the clathrin interactome. Additionally, several important candidate genes for schizophrenia, such as dysbindin, are involved in processes closely linked to CME and membrane trafficking. We discuss that key aspects of psychosis neuropathology such as synaptic dysfunction, white matter changes and aberrant neurodevelopment are all influenced by clathrin-dependent processes, and that other cellular trafficking mechanisms previously linked to psychoses interact with the clathrin interactome in important ways. Furthermore, many antipsychotic drugs have been shown to affect clathrin-interacting proteins. We propose that the targeted pharmacological manipulation of the clathrin interactome may offer fruitful opportunities for novel treatments of schizophrenia.