Publications by authors named "Pablo Cervantes"

Article Synopsis
  • - This study aimed to see if bipolar disorder (BD) patients' clinical profiles could be better differentiated by diagnostic subtype or by how they respond to lithium treatment.
  • - Researchers analyzed 477 adult BD patients treated with lithium for at least a year, using various clinical features to create clusters that reflected treatment responses and BD subtypes.
  • - The results indicated that the clusters provided more insight into lithium responsiveness than BD subtype, with a smaller cluster containing a higher percentage of lithium responders, suggesting that treatment response could be a more effective way to categorize these patients.
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Article Synopsis
  • Lithium is the primary treatment for bipolar disorder (BD), but how it works and predicts outcomes is not fully understood.
  • A previous study identified key cellular pathways linked to lithium response, including focal adhesion and PI3K-Akt signaling.
  • In this new study, researchers confirmed these pathways in a larger group of 2039 patients but found no connection with the extracellular matrix, suggesting that issues with neuronal growth signaling may impact lithium effectiveness.
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Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium.

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Article Synopsis
  • * Researchers examined 4,925 immune-related genes and their association with lithium treatment response and clinical features in a large bipolar patient sample.
  • * Findings indicate a few genetic associations with treatment response and clinical characteristics, revealing potential biomarkers, but overall support a weak connection between immune factors and bipolar disorder at a genetic level.
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Background: The distinction between bipolar I and bipolar II disorder and its treatment implications have been a matter of ongoing debate. The aim of this study was to examine differences between patients with bipolar I and II disorders with particular emphasis on the early phases of the disorders.

Methods: 808 subjects diagnosed with bipolar I (N = 587) or bipolar II disorder (N = 221) according to DSM-IV criteria were recruited between April 1994 and March 2022 from tertiary-level mood disorder clinics.

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Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores.

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Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores.

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Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients ( = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score.

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Background: 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.

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

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Article Synopsis
  • The study investigates the genetic and phenotypic traits associated with age at onset (AAO) and polarity at onset (PAO) in bipolar disorder to enhance understanding of the illness and develop screening tools.
  • Results indicate that an earlier AAO is linked to more severe symptoms, such as psychosis and suicidality, as well as variations in educational success and living situations.
  • The research reveals a significant relationship between higher polygenic risk scores for other mental disorders and earlier AAO, although no significant associations were found for PAO, highlighting considerable variability across different cohorts.
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Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes.

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Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region.

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Background: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk.

Methods: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH.

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Article Synopsis
  • Bipolar disorder has a genetic basis and complex causes; a large study compared nearly 42,000 bipolar patients with over 371,000 healthy controls, revealing 64 genomic regions linked to the disorder.
  • The findings showed that risk-related genes are heavily associated with brain functions, particularly in areas like the prefrontal cortex and hippocampus, and they include targets for various medications.
  • The research also distinguished between bipolar disorder types I and II, revealing a close genetic relationship and highlighting 15 specific genes that could lead to new treatment options and further investigations.
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Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data.

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Predicting lithium response (LiR) in bipolar disorder (BD) may inform treatment planning, but phenotypic heterogeneity complicates discovery of genomic markers. We hypothesized that patients with "exemplary phenotypes"-those whose clinical features are reliably associated with LiR and non-response (LiNR)-are more genetically separable than those with less exemplary phenotypes. Using clinical data collected from people with BD (n = 1266 across 7 centers; 34.

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Objective: Lithium remains an important treatment for mood disorders but is associated with kidney disease. Nephrogenic diabetes insipidus (NDI) is associated with up to 3-fold risk of incident chronic kidney disease among lithium users. There are limited randomized controlled trials (RCT) for treatments of lithium-induced NDI, and existing therapies can be poorly tolerated.

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Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLiGen) study.

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Background: The HOPE-BD was a naturalistic study established to follow individuals in Canada seeking treatment for bipolar disorder (BD). The study aimed to examine the course of BD and describe how clinical and sociodemographic factors are associated with outcomes.

Methods: Individuals with BD had their clinical data recorded at enrolment and were naturalistically treated.

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Objectives: Statins have recently been linked to having effects on cognition and mood in mood disorders, though results are mixed. In this paper, we use data from a recent randomized controlled trial (RCT) to examine the effect of statins on cognition and mood in patients with Bipolar Disorder (BD) and Major Depressive Disorder (MDD).

Methods: This is a secondary analysis of a randomized, double-blind, placebo-controlled clinical trial (n = 60) originally designed to examine the effect of atorvastatin (n = 27) versus placebo (n = 33) for lithium-induced diabetes insipidus in BD and MDD patients who were using lithium.

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Article Synopsis
  • * The analysis discovered 30 significant genetic loci linked to bipolar disorder, including 20 that hadn't been previously identified, which involve genes related to ion channels and neurotransmitter systems.
  • * The study also showed that Bipolar I disorder has a genetic connection to schizophrenia, particularly linked to psychosis, while Bipolar II disorder is more closely related to major depressive disorder, shedding light on potential biological mechanisms and clinical implications.
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